{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T18:05:57Z","timestamp":1775239557577,"version":"3.50.1"},"reference-count":263,"publisher":"Emerald","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,8,1]]},"abstract":"<jats:p>Causality has the potential to truly transform the way we solve a large number of real-world problems. Yet, so far, its potential largely remains to be unlocked as causality often requires crucial assumptions which cannot be tested in practice. To address this challenge, we propose a new way of thinking about causality-\u2013 we call this causal deep learning. Our causal deep learning framework spans three dimensions: (1) a structural dimension, which incorporates partial yet testable causal knowledge rather than assuming either complete or no causal knowledge among the variables of interest; (2) a parametric dimension, which encompasses parametric forms that capture the type of relationships among the variables of interest; and (3) a temporal dimension, which captures exposure times or how the variables of interest interact (possibly causally) over time. Our CDL framework enables us to precisely categorise and compare causal statistical learning methods. We use this categorisation to provide a comprehensive review of the CDL field. More importantly, CDL enables us to make progress on a variety of real-world problems by aiding us to leverage partial causal knowledge (including independencies among variables) and quantitatively characterising causal relationships among variables of interest (possibly over time). Our framework clearly identifies which assumptions are testable and which are not, so the resulting solutions can be judiciously adopted in practice. Our formulation helps us to combine or chain causal representations to solve specific problems without losing track of which assumptions are required to build these solutions, pushing real-world impact in healthcare, economics and business, environmental sciences and education, through causal deep learning.<\/jats:p>","DOI":"10.1561\/2000000123","type":"journal-article","created":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T05:24:21Z","timestamp":1722489861000},"page":"200-309","source":"Crossref","is-referenced-by-count":8,"title":["Causal Deep Learning: Encouraging Impact on Real-world Problems Through Causality"],"prefix":"10.1561","volume":"18","author":[{"given":"Jeroen","family":"Berrevoets","sequence":"first","affiliation":[{"name":"University of Cambridge ,","place":["UK"]}]},{"given":"Krzysztof","family":"Kacprzyk","sequence":"additional","affiliation":[{"name":"University of Cambridge ,","place":["UK"]}]},{"given":"Zhaozhi","family":"Qian","sequence":"additional","affiliation":[{"name":"University of Cambridge ,","place":["UK"]}]},{"given":"Mihaela","family":"van der Schaar","sequence":"additional","affiliation":[{"name":"University of Cambridge ,","place":["UK"]},{"name":"The Alan Turing Institute ,","place":["UK"]}]}],"member":"140","published-online":{"date-parts":[[2024,8,1]]},"reference":[{"key":"2026040313321947200_ref001","first-page":"10 549","volume-title":"Advances in Neural Information Processing Systems","author":"Aglietti","year":"2021"},{"key":"2026040313321947200_ref002","first-page":"3155","volume-title":"Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics","author":"Aglietti","year":"2020"},{"key":"2026040313321947200_ref003","doi-asserted-by":"crossref","DOI":"10.1097\/TP.0000000000003258","article-title":"Global transplantation covid report march 2020,","author":"Ahn","year":"2020","journal-title":"Transplantation"},{"key":"2026040313321947200_ref004","first-page":"372","article-title":"Interventional causal representation learning,","author":"Ahuja","year":"2023","journal-title":"International conference on machine learning"},{"issue":"6","key":"2026040313321947200_ref005","doi-asserted-by":"publisher","first-page":"716","DOI":"10.1109\/TAC.1974.1100705","article-title":"A new look at the statistical model identification,","volume":"19","author":"Akaike","year":"1974","journal-title":"IEEE Transactions on Automatic Control"},{"key":"2026040313321947200_ref006","article-title":"Bayesian inference of individualized treatment effects using multi-task gaussian processes,","volume":"30","author":"Alaa","year":"2017","journal-title":"Advances in neural information processing systems"},{"issue":"1","key":"2026040313321947200_ref007","doi-asserted-by":"crossref","first-page":"85","DOI":"10.12816\/0006075","article-title":"Knee osteoarthritis related pain: A narrative review of diagnosis and treatment,","volume":"8","author":"Alshami","year":"2014","journal-title":"International journal of health sciences"},{"key":"2026040313321947200_ref008","article-title":"Description of the recursive hybrid parents and children algorithm,","author":"Aussem","year":"2010"},{"key":"2026040313321947200_ref009","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2006.02579","author":"Bannon","year":"2020","journal-title":"Causality and batch reinforcement learning: Complementary approaches to planning in unknown domains"},{"key":"2026040313321947200_ref010","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1145\/3501714.3501743","article-title":"On pearl\u2019s hierarchy and the foundations of causal inference,","author":"Bareinboim","year":"2022","journal-title":"Probabilistic and Causal Inference: The Works of Judea Pearl"},{"key":"2026040313321947200_ref011","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1145\/3109859.3109922","article-title":"D\u00e9ja vu: The importance of time and causality in recommender systems,","author":"Basilico","year":"2017","journal-title":"Proceedings of the eleventh ACM conference on recommender systems"},{"issue":"4","key":"2026040313321947200_ref012","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1002\/jae.2343","article-title":"Estimating person-centered treatment (pet) effects using instrumental variables: An application to evaluating prostate cancer treatments,","volume":"29","author":"Basu","year":"2014","journal-title":"Journal of Applied Econometrics"},{"key":"2026040313321947200_ref013","first-page":"8226","article-title":"Dagma: Learning dags via m-matrices and a log-determinant acyclicity characterization,","volume":"35","author":"Bello","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"1","key":"2026040313321947200_ref014","doi-asserted-by":"crossref","first-page":"1024","DOI":"10.1038\/s41467-021-21056-z","article-title":"Causal network models of sars-cov-2 expression and aging to identify candidates for drug repurposing,","volume":"12","author":"Belyaeva","year":"2021","journal-title":"Nature communications"},{"key":"2026040313321947200_ref015","first-page":"792","article-title":"Learning queueing policies for organ transplantation allocation using interpretable counterfactual survival analysis,","volume":"139","author":"Berrevoets","year":"2021","journal-title":"Proceedings of the 38th International Conference on Machine Learning"},{"key":"2026040313321947200_ref016","article-title":"Disentangled counterfactual recurrent networks for treatment effect inference over time,","author":"Berrevoets","year":"2021","journal-title":"arXiv preprint arXiv:2112. 03811"},{"key":"2026040313321947200_ref017","article-title":"ODE discovery for longitudinal heterogeneous treatment effects inference,","author":"Berrevoets","year":"2024","journal-title":"The Twelfth International Conference on Learning Representations"},{"key":"2026040313321947200_ref018","first-page":"3568","article-title":"To impute or not to impute? missing data in treatment effect estimation,","author":"Berrevoets","year":"2023","journal-title":"International Conference on Artificial Intelligence and Statistics"},{"key":"2026040313321947200_ref019","doi-asserted-by":"crossref","DOI":"10.52202\/075280-0039","article-title":"Allsim: Simulating and benchmarking resource allocation policies in multi-user systems,","volume":"36","author":"Berrevoets","year":"2023","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2026040313321947200_ref020","first-page":"20 037","article-title":"Organite: Optimal transplant donor organ offering using an individual treatment effect,","volume":"33","author":"Berrevoets","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"2026040313321947200_ref021","article-title":"Differentiable and transportable structure learning,","author":"Berrevoets","year":"2022","journal-title":"arXiv preprint arXiv:2206.06354"},{"issue":"1","key":"2026040313321947200_ref022","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1515\/jci-2020-0009","article-title":"Treatment effect optimisation in dynamic environments,","volume":"10","author":"Berrevoets","year":"2022","journal-title":"Journal of Causal Inference"},{"key":"2026040313321947200_ref023","first-page":"2314","article-title":"Differentiable causal discovery under unmeasured confounding,","author":"Bhattacharya","year":"2021","journal-title":"International Conference on Artificial Intelligence and Statistics"},{"key":"2026040313321947200_ref024","article-title":"Estimating counterfactual treatment outcomes over time through adversarially balanced representations,","author":"Bica","year":"2020","journal-title":"International Conference on Learning Representations"},{"key":"2026040313321947200_ref025","first-page":"16 434","article-title":"Estimating the effects of continuous-valued interventions using generative adversarial networks,","volume":"33","author":"Bica","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2026040313321947200_ref026","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2202.05100","article-title":"Adaptively exploiting d-separators with causal bandits,","author":"Bilodeau","year":"2022"},{"issue":"2","key":"2026040313321947200_ref027","doi-asserted-by":"crossref","first-page":"e018320","DOI":"10.1136\/bmjopen-2017-018320","article-title":"Adaptive design clinical trials: A review of the literature and clinicaltrials. gov,","volume":"8","author":"Bothwell","year":"2018","journal-title":"BMJ open"},{"key":"2026040313321947200_ref028","doi-asserted-by":"crossref","first-page":"38 319","DOI":"10.52202\/068431-2776","article-title":"Weakly supervised causal representation learning,","volume":"35","author":"Brehmer","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2026040313321947200_ref029","first-page":"21 865","article-title":"Differentiable causal discovery from interventional data,","volume":"33","author":"Brouillard","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"18","key":"2026040313321947200_ref030","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1016\/j.ifacol.2016.10.249","article-title":"Sparse identification of nonlinear dynamics with control (sindyc),","volume":"49","author":"Brunton","year":"2016","journal-title":"IFAC- PapersOnLine"},{"issue":"3","key":"2026040313321947200_ref031","first-page":"404","article-title":"Invariance, causality and robustness,","volume":"35","author":"B\u00fchlmann","year":"2020","journal-title":"Statistical Science"},{"issue":"5","key":"2026040313321947200_ref032","doi-asserted-by":"crossref","first-page":"1190","DOI":"10.1137\/0916069","article-title":"A limited memory algorithm for bound constrained optimization,","volume":"16","author":"Byrd","year":"1995","journal-title":"SIAM Journal on scientific computing"},{"key":"2026040313321947200_ref033","article-title":"Accelerating bayesian inference over nonlinear differential equations with gaussian processes,","volume":"21","author":"Calderhead","year":"2008","journal-title":"Advances in neural information processing systems"},{"issue":"6","key":"2026040313321947200_ref034","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1002\/(SICI)1099-131X(199711)16:6<425::AID-FOR657>3.0.CO;2-9","article-title":"Causality and forecasting in incomplete systems,","volume":"16","author":"Caporale","year":"1997","journal-title":"Journal of Forecasting"},{"issue":"7","key":"2026040313321947200_ref035","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1056\/NEJMsa1407211","article-title":"Smoking and mortality \u2014 beyond established causes,","volume":"372","author":"Carter","journal-title":"New England Journal of Medicine"},{"key":"2026040313321947200_ref036","article-title":"Synthetic model combination: A new machine learning method for pharmacometric model ensembling,","author":"Chan","year":"2023","journal-title":"CPT: pharmacometrics & systems pharmacology"},{"key":"2026040313321947200_ref037","first-page":"2021","article-title":"Drug repurposing of metformin for alzheimer\u2019s disease: Combining causal inference in medical records data and systems pharmacology for biomarker identification,","author":"Charpignon","year":"2021","journal-title":"medRxiv"},{"issue":"1","key":"2026040313321947200_ref038","doi-asserted-by":"crossref","first-page":"7652","DOI":"10.1038\/s41467-022-35157-w","article-title":"Causal inference in medical records and complementary systems pharmacology for metformin drug re-purposing towards dementia,","volume":"13","author":"Charpignon","year":"2022","journal-title":"Nature communications"},{"key":"2026040313321947200_ref039","first-page":"19 314","article-title":"Iterative deep graph learning for graph neural networks: Better and robust node embeddings,","volume":"33","author":"Chen","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2026040313321947200_ref040","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1109\/CogMI52975.2021.00016","article-title":"Boosting synthetic data generation with effective nonlinear causal discovery,","author":"Cinquini","year":"2021","journal-title":"2021 IEEE Third International Conference on Cognitive Machine Intelligence (CogMI)"},{"issue":"5","key":"2026040313321947200_ref041","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1136\/adc.79.5.410","article-title":"The first randomised controlled trial,","volume":"79","author":"Craft","year":"1998","journal-title":"Archives of Disease in Childhood"},{"issue":"1","key":"2026040313321947200_ref042","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1109\/TPAMI.1983.4767341","article-title":"Markov random field texture models,","author":"Cross","year":"1983","journal-title":"IEEE Transactions on pattern analysis and machine intelligence"},{"key":"2026040313321947200_ref043","first-page":"6603","article-title":"Adaptive identification of populations with treatment benefit in clinical trials: Machine learning challenges and solutions,","volume":"202","author":"Curth","year":"2023","journal-title":"Proceedings of the 40th International Conference on Machine Learning"},{"key":"2026040313321947200_ref044","article-title":"Really doing great at estimating cate? a critical look at ml bench-marking practices in treatment effect estimation,","author":"Curth","year":"2021","journal-title":"Thirty-fifth conference on neural information processing systems datasets and benchmarks track (round 2)"},{"key":"2026040313321947200_ref045","first-page":"1810","article-title":"Nonparametric estimation of heterogeneous treatment effects: From theory to learning algorithms,","author":"Curth","year":"2021","journal-title":"International Conference on Artificial Intelligence and Statistics"},{"key":"2026040313321947200_ref046","first-page":"15 883","article-title":"On inductive biases for heterogeneous treatment effect estimation,","volume":"34","author":"Curth","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2026040313321947200_ref047","article-title":"Causal reasoning from meta-reinforcement learning,","author":"Dasgupta","year":"2019","journal-title":"arXiv preprint arXiv:1901.08162"},{"issue":"2","key":"2026040313321947200_ref048","doi-asserted-by":"crossref","first-page":"1939","DOI":"10.1214\/15-EJS1053","article-title":"Optimal rate of direct estimators in systems of ordinary differential equations linear in functions of the parameters,","volume":"9","author":"Dattner","year":"2015","journal-title":"Electronic Journal of Statistics"},{"key":"2026040313321947200_ref049","article-title":"Causal confusion in imitation learning,","volume":"32","author":"De Haan","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2026040313321947200_ref050","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1109\/CVPR.2009.5206848","article-title":"Imagenet: A large-scale hierarchical image database,","author":"Deng","year":"2009","journal-title":"2009 IEEE conference on computer vision and pattern recognition"},{"key":"2026040313321947200_ref051","volume-title":"Pattern classification and scene analysis","author":"Duda","year":"1973"},{"issue":"2","key":"2026040313321947200_ref052","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s41060-016-0038-6","article-title":"Introduction to the foundations of causal discovery,","volume":"3","author":"Eberhardt","year":"2017","journal-title":"International Journal of Data Science and Analytics"},{"issue":"2","key":"2026040313321947200_ref053","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1111\/jtsa.12213","article-title":"Graphical modeling for multivariate hawkes processes with nonparametric link functions,","volume":"38","author":"Eichler","year":"2017","journal-title":"Journal of Time Series Analysis"},{"key":"2026040313321947200_ref054","author":"Emanuel","year":"2020","journal-title":"Fair allocation of scarce medical resources in the time of covid-19"},{"issue":"4","key":"2026040313321947200_ref055","doi-asserted-by":"crossref","first-page":"1146","DOI":"10.1109\/18.243433","article-title":"Strong universal consistency of neural network classifiers,","volume":"39","author":"Farag\u00f3","year":"1993","journal-title":"IEEE Transactions on Information Theory"},{"key":"2026040313321947200_ref056","first-page":"22 667","article-title":"Slaps: Self-supervision improves structure learning for graph neural networks,","volume":"34","author":"Fatemi","year":"2021","journal-title":"Advances"},{"key":"2026040313321947200_ref057","doi-asserted-by":"crossref","first-page":"327","DOI":"10.2307\/2111021","article-title":"Granger causality and the times series analysis of political relationships,","author":"Freeman","year":"1983","journal-title":"American Journal of Political Science"},{"key":"2026040313321947200_ref058","article-title":"Causal reinforcement learning using observational and interventional data,","author":"Gasse","year":"2021","journal-title":"arXiv preprint arXiv:2106.14421"},{"key":"2026040313321947200_ref059","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/B978-1-55860-332-5.50035-3","article-title":"Learning gaussian networks,","author":"Geiger","year":"1994","journal-title":"Uncertainty Proceedings 1994"},{"key":"2026040313321947200_ref060","first-page":"3","volume":"9","author":"Geiger","year":"1990","journal-title":"Machine Intelligence and Pattern Recognition"},{"key":"2026040313321947200_ref061","first-page":"196","article-title":"Prince: Provider-side interpretability with counterfactual explanations in recommender systems,","author":"Ghazimatin","year":"2020","journal-title":"Proceedings of the 13th International"},{"key":"2026040313321947200_ref062","doi-asserted-by":"crossref","first-page":"524","DOI":"10.3389\/fgene.2019.00524","article-title":"Review of causal discovery methods based on graphical models,","volume":"10","author":"Glymour","year":"2019","journal-title":"Frontiers in genetics"},{"key":"2026040313321947200_ref063","author":"Goodfellow","year":"2016","journal-title":"Deep learning"},{"key":"2026040313321947200_ref064","article-title":"Causal generative neural networks,","author":"Goudet","year":"2017","journal-title":"arXiv preprint arXiv:1711.08936"},{"key":"2026040313321947200_ref065","doi-asserted-by":"crossref","first-page":"424","DOI":"10.2307\/1912791","article-title":"Investigating causal relations by econometric models and cross-spectral methods,","author":"Granger","year":"1969","journal-title":"Econometrica: journal of the Econometric Society"},{"key":"2026040313321947200_ref066","first-page":"5880","article-title":"Counterfactual regression with importance sampling weights.,","author":"Hassanpour","year":"2019","journal-title":"IJCAI"},{"issue":"1","key":"2026040313321947200_ref067","first-page":"291","article-title":"Jointly interventional and observational data: Estimation of interventional markov equivalence classes of directed acyclic graphs,","volume":"77","author":"Hauser","year":"2015","journal-title":"Journal of the Royal Statistical"},{"key":"2026040313321947200_ref068","article-title":"Causal counterfactuals for improving the robustness of reinforcement learning,","author":"He","year":"2022","journal-title":"arXiv preprint arXiv:2211.05551"},{"key":"2026040313321947200_ref069","first-page":"33","article-title":"A tutorial on learning with bayesian networks,","author":"Heckerman","year":"2008","journal-title":"Innovations in Bayesian networks: Theory and applications"},{"key":"2026040313321947200_ref070","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1023\/A:1022623210503","article-title":"Learning bayesian networks: The combination of knowledge and statistical data,","volume":"20","author":"Heckerman","year":"1995","journal-title":"Machine learning"},{"key":"2026040313321947200_ref071","first-page":"1","article-title":"A bayesian approach to causal discovery,","author":"Heckerman","year":"2006","journal-title":"Innovations in Machine Learning"},{"issue":"624","key":"2026040313321947200_ref072","doi-asserted-by":"publisher","first-page":"372","DOI":"10.3399\/bjgp14X680749","article-title":"Research into practice: Improving musculoskeletal care in general practice,","volume":"64","author":"Helliwell","year":"2014","journal-title":"British Journal of General Practice"},{"key":"2026040313321947200_ref073","article-title":"Improving fair predictions using variational inference in causal models,","author":"Helwegen","year":"2020","journal-title":"arXiv preprint arXiv:2008.10880"},{"key":"2026040313321947200_ref074","doi-asserted-by":"crossref","first-page":"6910","DOI":"10.1609\/aaai.v36i6.20648","article-title":"Reinforcement learning of causal variables using mediation analysis,","author":"Herlau","year":"2022","journal-title":"36th AAAI Conference on Artificial Intelligence"},{"issue":"1","key":"2026040313321947200_ref075","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1198\/jcgs.2010.08162","article-title":"Bayesian nonparametric modeling for causal inference,","volume":"20","author":"Hill","year":"2011","journal-title":"Journal of Computational and Graphical Statistics"},{"issue":"396","key":"2026040313321947200_ref076","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1080\/01621459.1986.10478354","article-title":"Statistics and causal inference,","volume":"81","author":"Holland","year":"1986","journal-title":"Journal of the American statistical Association"},{"key":"2026040313321947200_ref077","article-title":"TabPFN: A transformer that solves small tabular classification problems in a second,","author":"Hollmann","year":"2022","journal-title":"arXiv preprint arXiv:2207.01848"},{"key":"2026040313321947200_ref078","article-title":"Nonlinear causal discovery with additive noise models,","volume":"21","author":"Hoyer","year":"2008","journal-title":"Advances"},{"key":"2026040313321947200_ref079","volume-title":"International Conference on Artificial Intelligence and Statistics","author":"Huynh","year":"2024"},{"issue":"1984","key":"2026040313321947200_ref080","doi-asserted-by":"crossref","first-page":"20 110 534","DOI":"10.1098\/rsta.2011.0534","article-title":"Independent component analysis: Recent advances,","volume":"371","author":"Hyv\u00e4rinen","year":"2013","journal-title":"Philosophical Transactions of the Royal Society A: Mathematical,"},{"issue":"4-5","key":"2026040313321947200_ref081","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/S0893-6080(00)00026-5","article-title":"Independent component analysis: Algorithms and applications,","volume":"13","author":"Hyv\u00e4rinen","year":"2000","journal-title":"Neural networks"},{"issue":"3","key":"2026040313321947200_ref082","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/S0893-6080(98)00140-3","article-title":"Nonlinear independent component analysis: Existence and uniqueness results,","volume":"12","author":"Hyv\u00e4rinen","year":"1999","journal-title":"Neural networks"},{"issue":"4","key":"2026040313321947200_ref083","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1257\/jel.20191597","article-title":"Potential outcome and directed acyclic graph approaches to causality: Relevance for empirical practice in economics,","volume":"58","author":"Imbens","year":"2020","journal-title":"Journal of Economic Literature"},{"key":"2026040313321947200_ref084","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9781139025751","volume-title":"Causal inference in statistics, social, and biomedical sciences","author":"Imbens","year":"2015"},{"key":"2026040313321947200_ref085","doi-asserted-by":"crossref","first-page":"3561","DOI":"10.1145\/3394486.3406477","article-title":"Overview and importance of data quality for machine learning tasks,","author":"Jain","year":"2020","journal-title":"Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining"},{"issue":"3","key":"2026040313321947200_ref086","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/0165-1765(80)90024-5","article-title":"Efficient tests for normality, homoscedasticity and serial independence of regression residuals,","volume":"6","author":"Jarque","year":"1980","journal-title":"Economics letters"},{"key":"2026040313321947200_ref087","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.knosys.2011.08.010","article-title":"Improving tree augmented naive bayes for class probability estimation,","author":"Jiang","year":"2012","journal-title":"Knowledge-Based Systems"},{"key":"2026040313321947200_ref088","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.engappai.2016.02.002","article-title":"Deep feature weighting for naive bayes and its application to text classification,","volume":"52","author":"Jiang","year":"2016","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"10","key":"2026040313321947200_ref089","doi-asserted-by":"crossref","first-page":"1361","DOI":"10.1109\/TKDE.2008.234","article-title":"A novel bayes model: Hidden naive bayes,","volume":"21","author":"Jiang","year":"2008","journal-title":"IEEE Transactions on knowledge and data engineering"},{"issue":"3","key":"2026040313321947200_ref090","first-page":"279","article-title":"The potential and pitfalls of artificial intelligence in clinical pharmacology,","volume":"12","author":"Johnson","year":"2023","journal-title":"CPT: Pharmacometrics & Systems Pharmacology"},{"issue":"3","key":"2026040313321947200_ref091","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1002\/hep.21563","article-title":"The model for end-stage liver disease (meld),","volume":"45","author":"Kamath","year":"2007","journal-title":"Hepatology"},{"key":"2026040313321947200_ref092","first-page":"2021","article-title":"Evaluating the heterogeneous effect of extended incubation to blastocyst transfer on the implantation outcome via causal inference,","author":"Kan-Tor","year":"2021","journal-title":"bioRxiv"},{"key":"2026040313321947200_ref093","first-page":"10 676","volume-title":"Proceedings of the 39th International Conference on Machine Learning","author":"Kancheti","year":"2022"},{"key":"2026040313321947200_ref094","article-title":"Learning interpretable models with causal guarantees,","author":"Kim","year":"2019","journal-title":"arXiv preprint arXiv:1901.08576"},{"key":"2026040313321947200_ref095","article-title":"CausalGAN: Learning causal implicit generative models with adversarial training,","author":"Kocaoglu","year":"2018","journal-title":"International Conference on Learning Representations"},{"key":"2026040313321947200_ref096","first-page":"202","article-title":"Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid,","volume":"96","author":"Kohavi","year":"1996","journal-title":"Kdd"},{"key":"2026040313321947200_ref097","volume-title":"Probabilistic graphical models: principles","author":"Koller","year":"2009"},{"key":"2026040313321947200_ref098","first-page":"407","volume-title":"Proceedings of the First Conference on Causal Learning and Reasoning","author":"Kroon","year":"2022"},{"issue":"11","key":"2026040313321947200_ref099","doi-asserted-by":"crossref","first-page":"1553","DOI":"10.14778\/3342263.3342633","article-title":"An intermediate representation for optimizing machine learning pipelines,","volume":"12","author":"Kunft","year":"2019","journal-title":"Proceedings of the VLDB Endowment"},{"issue":"10","key":"2026040313321947200_ref100","doi-asserted-by":"crossref","first-page":"4156","DOI":"10.1073\/pnas.1804597116","article-title":"Metalearners for estimating heterogeneous treatment effects using machine learning,","volume":"116","author":"K\u00fcnzel","year":"2019","journal-title":"Proceedings of the national academy of sciences"},{"key":"2026040313321947200_ref101","article-title":"Counterfactual fairness,","volume":"30","author":"Kusner","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"2026040313321947200_ref102","article-title":"Selecting treatment effects models for domain adaptation using causal knowledge,","author":"Kyono","year":"2021","journal-title":"arXiv preprint arXiv:2102.06271"},{"key":"2026040313321947200_ref103","article-title":"Improving model robustness using causal knowledge,","author":"Kyono","year":"2019","journal-title":"arXiv preprint arXiv:1911.12441"},{"issue":"6","key":"2026040313321947200_ref104","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1109\/TAI.2021.3101185","article-title":"Exploiting causal structure for robust model selection in unsupervised domain adaptation,","volume":"2","author":"Kyono","year":"2021","journal-title":"IEEE Transactions on Artificial Intelligence"},{"key":"2026040313321947200_ref105","first-page":"23 806","article-title":"Miracle: Causally-aware imputation via learning missing data mechanisms,","volume":"34","author":"Kyono","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2026040313321947200_ref106","first-page":"1501","article-title":"Castle: Regularization via auxiliary causal graph discovery,","volume":"33","author":"Kyono","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2026040313321947200_ref107","first-page":"223","article-title":"An analysis of bayesian classifiers,","volume":"90","author":"Langley","year":"1992","journal-title":"Aaai"},{"key":"2026040313321947200_ref108","volume-title":"Advances in Neural Information Processing Systems","author":"Lattimore","year":"2016"},{"key":"2026040313321947200_ref109","article-title":"Causal bandits: Learning good interventions via causal inference,","volume":"29","author":"Lattimore","year":"2016","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2026040313321947200_ref110","volume-title":"Advances in Neural Information Processing Systems","author":"Lee","year":"2018"},{"issue":"01","key":"2026040313321947200_ref111","doi-asserted-by":"publisher","first-page":"4164","DOI":"10.1609\/aaai.v33i01.33014164","article-title":"Structural causal bandits with non-manipulable variables,","volume":"33","author":"Lee","year":"2019","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"2026040313321947200_ref112","first-page":"8565","volume-title":"Advances in Neural Information Processing Systems","author":"Lee","year":"2020"},{"key":"2026040313321947200_ref113","doi-asserted-by":"publisher","first-page":"217 917","DOI":"10.1109\/ACCESS.2020.3042180","article-title":"An enhanced naive bayes model for dissolved oxygen forecasting in shellfish aquaculture,","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"2026040313321947200_ref114","volume-title":"Advances in Neural Information Processing Systems","author":"Li","year":"2022"},{"key":"2026040313321947200_ref115","article-title":"Forecasting treatment responses over time using recurrent marginal structural networks,","volume":"31","author":"Lim","year":"2018","journal-title":"Advances in neural information"},{"key":"2026040313321947200_ref116","article-title":"Causal representation learning for instantaneous and temporal effects in interactive systems,","author":"Lippe","year":"2022","journal-title":"The Eleventh International Conference on Learning Representations"},{"key":"2026040313321947200_ref117","article-title":"When deep learning meets causal inference: A computational framework for drug repurposing from real-world data,","author":"Liu","year":"2020","journal-title":"arXiv preprint arXiv:2007.10152"},{"issue":"1","key":"2026040313321947200_ref118","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1038\/s42256-020-00276-w","article-title":"A deep learning framework for drug repurposing via emulating clinical trials on real-world patient data,","volume":"3","author":"Liu","year":"2021","journal-title":"Nature machine intelligence"},{"key":"2026040313321947200_ref119","article-title":"GOGGLE: Generative modelling for tabular data by learning relational structure,","author":"Liu","year":"2023","journal-title":"International Conference on Learning Representations"},{"key":"2026040313321947200_ref120","first-page":"17 081","article-title":"Towards robust and adaptive motion forecasting: A causal representation perspective,","author":"Liu","year":"2022","journal-title":"Proceedings of the IEEE\/CVF Conference"},{"key":"2026040313321947200_ref121","article-title":"Causal reasoning for algorithmic fairness,","author":"Loftus","year":"2018","journal-title":"arXiv preprint arXiv:1805.05859"},{"key":"2026040313321947200_ref122","article-title":"Invariant causal representation learning for generalization in imitation and reinforcement learning,","author":"Lu","year":"2022","journal-title":"ICLR2022 Workshop on the Elements of Reasoning: Objects, Structure and Causality"},{"key":"2026040313321947200_ref123","first-page":"24 817","volume-title":"Advances in Neural Information Processing Systems","author":"Lu","year":"2021"},{"key":"2026040313321947200_ref124","first-page":"526","article-title":"Efficient reinforcement learning with prior causal knowledge,","volume":"177","author":"Lu","year":"2022","journal-title":"Proceedings of the First Conference on Causal Learning and Reasoning"},{"key":"2026040313321947200_ref125","first-page":"141","volume-title":"Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI)","author":"Lu","year":"2020"},{"key":"2026040313321947200_ref126","article-title":"Resolving causal confusion in reinforcement learning via robust exploration,","author":"Lyle","year":"2021","journal-title":"Self-Supervision for Reinforcement Learning Workshop-ICLR 2021"},{"key":"2026040313321947200_ref127","doi-asserted-by":"publisher","DOI":"10.3389\/fbioe.2015.00180","article-title":"Gradient matching methods for computational inference in mechanistic models for systems biology: A review and comparative analysis,","volume":"3","author":"Macdonald","year":"2015","journal-title":"Frontiers in Bioengineering"},{"key":"2026040313321947200_ref128","article-title":"Causal transfer learning,","author":"Magliacane","year":"2017","journal-title":"arXiv preprint arXiv:1707.06422"},{"key":"2026040313321947200_ref129","article-title":"A causal bandit approach to learning good atomic interventions in presence of unobserved confounders,","author":"Maiti","year":"2022","journal-title":"The 38th Conference on Uncertainty in Artificial Intelligence"},{"key":"2026040313321947200_ref130","first-page":"3947","article-title":"Generative interventions for causal learning,","author":"Mao","year":"2021","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"2026040313321947200_ref131","doi-asserted-by":"crossref","DOI":"10.1214\/20-AOAS1356","article-title":"Doubly robust treatment effect estimation with missing attributes,","author":"Mayer","year":"2020"},{"issue":"5948","key":"2026040313321947200_ref132","doi-asserted-by":"crossref","first-page":"1705","DOI":"10.1126\/science.1175570","article-title":"Optimizing influenza vaccine distribution,","volume":"325","author":"Medlock","year":"2009","journal-title":"Science"},{"key":"2026040313321947200_ref133","volume-title":"Complete orientation rules for patterns","author":"Meek","year":"1995"},{"key":"2026040313321947200_ref134","article-title":"Strong completeness and faithfulness in bayesian networks,","author":"Meek","year":"2013","journal-title":"arXiv preprint arXiv:1302.4973"},{"key":"2026040313321947200_ref135","first-page":"421","volume-title":"Proceedings of The 11th International Conference on Probabilistic Graphical Models","author":"M\u00e9ndez-Molina","year":"2022"},{"key":"2026040313321947200_ref136","article-title":"Learning large scale ordinary differential equation systems,","author":"Mikkelsen","year":"2017","journal-title":"arXiv preprint arXiv:1710.09308"},{"key":"2026040313321947200_ref137","article-title":"Representation learning via invariant causal mechanisms,","author":"Mitrovic","year":"2020","journal-title":"arXiv preprint arXiv:2010.07922"},{"issue":"7540","key":"2026040313321947200_ref138","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning,","volume":"518","author":"Mnih","year":"2015","journal-title":"nature"},{"issue":"534","key":"2026040313321947200_ref139","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1080\/01621459.2021.1874961","article-title":"Graphical models for processing missing data,","volume":"116","author":"Mohan","year":"2021","journal-title":"Journal of the American Statistical Association"},{"key":"2026040313321947200_ref140","article-title":"Graphical models for inference with missing data,","volume":"26","author":"Mohan","year":"2013","journal-title":"Advances in neural information processing systems"},{"issue":"1","key":"2026040313321947200_ref141","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1145\/3400051.3400058","article-title":"Causal interpretability for machine learning-problems, methods and evaluation,","volume":"22","author":"Moraffah","year":"2020","journal-title":"ACM SIGKDD Explorations Newsletter"},{"key":"2026040313321947200_ref142","article-title":"Simultaneous missing value imputation and structure learning with groups,","author":"Morales-Alvarez","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2026040313321947200_ref143","article-title":"Vicause: Simultaneous missing value imputation and causal discovery with groups,","author":"Morales-Alvarez","year":"2021","journal-title":"arXiv preprint arXiv:2110.08223"},{"key":"2026040313321947200_ref144","first-page":"2020","article-title":"The extraordinary decisions facing italian doctors,","volume":"11","author":"Mounk","year":"2020","journal-title":"The Atlantic"},{"key":"2026040313321947200_ref145","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/978-3-030-92659-5_6","article-title":"Learning robust models using the principle of independent causal mechanisms,","author":"M\u00fcller","year":"2021","journal-title":"DAGM German Conference on Pattern Recognition"},{"key":"2026040313321947200_ref146","volume-title":"Machine learning: a probabilistic perspective","author":"Murphy","year":"2012"},{"key":"2026040313321947200_ref147","article-title":"Provably efficient causal model-based reinforcement learning for environment-agnostic generalization,","author":"Mutti","year":"2022","journal-title":"A causal view on dynamical systems, NeurIPS 2022 workshop"},{"key":"2026040313321947200_ref148","doi-asserted-by":"crossref","first-page":"109 088","DOI":"10.1016\/j.spl.2021.109088","article-title":"On the tight constant in the multivariate Dvoretzky-Kiefer-Wolfowitz inequality,","volume":"173","author":"M. Naaman","year":"2021","journal-title":"Statistics & Probability Letters"},{"key":"2026040313321947200_ref149","first-page":"2017","volume-title":"Proceedings of The 24th International Conference","author":"Nair","year":"2021"},{"key":"2026040313321947200_ref150","article-title":"A large-scale observational study of the causal effects of a behavioral health nudge,","author":"Nazaret","year":"2022","journal-title":"NeurIPS 2022 Workshop on Causality for Real-world Impact"},{"key":"2026040313321947200_ref151","first-page":"1","article-title":"Sur les applications de la th\u00e9orie des probabilit\u00e9s aux experiences agricoles: Essai des principes,","volume":"10","author":"Neyman","year":"1923","journal-title":"Roczniki Nauk Rolniczych"},{"key":"2026040313321947200_ref152","author":"Ng","year":"2021","journal-title":"NeurIPS workshop on datacentric ai"},{"key":"2026040313321947200_ref153","first-page":"8176","article-title":"On the convergence of continuous constrained optimization for structure learning,","author":"Ng","year":"2022","journal-title":"International Conference on Artificial Intelligence and Statistics"},{"key":"2026040313321947200_ref154","doi-asserted-by":"crossref","first-page":"bbad120","DOI":"10.1093\/bib\/bbad120","article-title":"Machine learning for synergistic network pharmacology: A comprehensive overview,","author":"Noor","year":"2023","journal-title":"Briefings in Bioinformatics"},{"issue":"17","key":"2026040313321947200_ref155","doi-asserted-by":"publisher","first-page":"i468","DOI":"10.1093\/bioinformatics\/btu452","article-title":"Causal network inference using biochemical kinetics,","volume":"30","author":"Oates","year":"2014","journal-title":"Bioinformatics"},{"issue":"1\/2","key":"2026040313321947200_ref156","doi-asserted-by":"crossref","first-page":"100","DOI":"10.2307\/2333009","article-title":"Continuous inspection schemes,","volume":"41","author":"Page","year":"1954","journal-title":"Biometrika"},{"key":"2026040313321947200_ref157","first-page":"1595","article-title":"Dynotears: Structure learning from time-series data,","author":"Pamfil","year":"2020","journal-title":"International Conference on Artificial Intelligence and Statistics"},{"key":"2026040313321947200_ref158","volume-title":"NeurIPS workshop on causal machine learning for real-world impact (CML4Impact)","author":"Pawlowski","year":"2022"},{"key":"2026040313321947200_ref159","volume-title":"Probabilistic reasoning in intelligent systems: networks of plausible inference","author":"Pearl","year":"1988"},{"key":"2026040313321947200_ref160","volume-title":"Foundations of bayesianism","author":"Pearl","year":"2001"},{"key":"2026040313321947200_ref161","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511803161","volume-title":"Causality","author":"Pearl","year":"2009"},{"key":"2026040313321947200_ref162","volume-title":"The book of why: the new science of cause and effect","author":"Pearl","year":"2018"},{"key":"2026040313321947200_ref163","first-page":"789","volume-title":"Studies in Logic and the Foundations of Mathematics","author":"Pearl","year":"1995"},{"key":"2026040313321947200_ref164","volume-title":"Machine learning of motor skills for robotics","author":"Peters","year":"2007"},{"key":"2026040313321947200_ref165","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1145\/3501714.3501752","article-title":"Causal models for dynamical systems,","author":"Peters","year":"2022","journal-title":"Probabilistic and Causal Inference: The Works of Judea Pearl"},{"key":"2026040313321947200_ref166","volume-title":"Elements of causal inference: foundations and learning algorithms","author":"Peters","year":"2017"},{"issue":"58","key":"2026040313321947200_ref167","first-page":"2009","article-title":"Causal discovery with continuous additive noise models,","volume":"15","author":"Peters","year":"2014","journal-title":"Journal of Machine Learning Research"},{"key":"2026040313321947200_ref168","first-page":"1810","article-title":"Identifying causal structure in large-scale kinetic systems,","author":"Pfister","year":"2018","journal-title":"arXiv"},{"issue":"527","key":"2026040313321947200_ref169","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1080\/01621459.2018.1491403","article-title":"Invariant causal prediction for sequential data,","volume":"114","author":"Pfister","year":"2019","journal-title":"Journal of the American Statistical Association"},{"key":"2026040313321947200_ref170","first-page":"11 364","article-title":"Integrating expert odes into neural odes: Pharmacology and disease progression,","volume":"34","author":"Qian","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"486","key":"2026040313321947200_ref171","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1198\/jasa.2009.0042","article-title":"Intervention and causality: Forecasting traffic flows using a dynamic bayesian network,","volume":"104","author":"Queen","year":"2009","journal-title":"Journal of the American Statistical Association"},{"issue":"5","key":"2026040313321947200_ref172","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1111\/j.1467-9868.2007.00610.x","article-title":"Parameter estimation for differential equations: A generalized smoothing approach,","volume":"69","author":"Ramsay","year":"2007","journal-title":"Journal of the Royal Statistical Society: Series B (Statistical Methodology)"},{"key":"2026040313321947200_ref173","first-page":"27 772","article-title":"Beware of the simulated dag! causal discovery benchmarks may be easy to game,","volume":"34","author":"Reisach","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"30","key":"2026040313321947200_ref174","first-page":"2013","article-title":"Single world intervention graphs (swigs): A unification of the counterfactual and graphical approaches to causality,","volume":"128","author":"Richardson","year":"2013","journal-title":"Center for the Statistics and the Social"},{"issue":"5","key":"2026040313321947200_ref175","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/0005-1098(78)90005-5","article-title":"Modeling by shortest data description,","volume":"14","author":"Rissanen","year":"1978","journal-title":"Automatica"},{"key":"2026040313321947200_ref176","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1097\/00001648-200009000-00011","article-title":"Marginal structural models and causal inference in epidemiology,","author":"Robins","year":"2000","journal-title":"Epidemiology"},{"issue":"1","key":"2026040313321947200_ref177","first-page":"1309","article-title":"Invariant models for causal transfer learning,","volume":"19","author":"Rojas-Carulla","year":"2018","journal-title":"The Journal of Machine Learning Research"},{"issue":"6","key":"2026040313321947200_ref178","doi-asserted-by":"publisher","first-page":"1147","DOI":"10.5114\/aoms.2014.47825","article-title":"Indications requiring preoperative magnetic resonance imaging before knee arthroscopy,","volume":"10","author":"Ro\u00dfbach","year":"2014","journal-title":"Archives of medical science : AMS"},{"issue":"4","key":"2026040313321947200_ref179","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1214\/ss\/1177012032","article-title":"Comment: Neyman (1923) and causal inference in experiments and observational studies,","volume":"5","author":"Rubin","year":"1990","journal-title":"Statistical Science"},{"issue":"371","key":"2026040313321947200_ref180","doi-asserted-by":"publisher","first-page":"591","DOI":"10.2307\/2287653","article-title":"Comment on \"randomization analysis of experimental data: The fisher randomization test\",","volume":"75","author":"Rubin","year":"1980","journal-title":"Journal of the American Statistical Association"},{"key":"2026040313321947200_ref181","article-title":"Causal discovery and injection for feed-forward neural networks,","author":"Russo","year":"2022","journal-title":"arXiv preprint arXiv:2205.09787"},{"issue":"148","key":"2026040313321947200_ref182","first-page":"1","article-title":"Faithfulness of probability distributions and graphs,","volume":"18","author":"Sadeghi","year":"2017","journal-title":"Journal of Machine Learning Research"},{"key":"2026040313321947200_ref183","first-page":"1","article-title":"\u201ceveryone wants to do the model work, not the data work","author":"Sambasivan","year":"2021","journal-title":"proceedings of the 2021 CHI Conference on Human Factors in Computing Systems"},{"key":"2026040313321947200_ref184","article-title":"Counterfactual generative networks,","author":"Sauer","year":"2021","journal-title":"International Conference on Learning Representations"},{"key":"2026040313321947200_ref185","article-title":"A meta-reinforcement learning algorithm for causal discovery,","author":"Sauter","year":"2022","journal-title":"UAI 2022 Workshop on Causal Representation Learning"},{"issue":"8","key":"2026040313321947200_ref186","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and differentiation of data by simplified least squares procedures.,","volume":"36","author":"Savitzky","year":"1964","journal-title":"Analytical chemistry"},{"issue":"4","key":"2026040313321947200_ref187","first-page":"1875","article-title":"Nonparametric regression using deep neural networks with relu activation function,","volume":"48","author":"Schmidt-Hieber","year":"2020","journal-title":"The Annals of Statistics"},{"issue":"5","key":"2026040313321947200_ref188","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1109\/JPROC.2021.3058954","article-title":"Toward causal representation learn-ing,","volume":"109","author":"Sch\u00f6lkopf","year":"2021","journal-title":"Proceedings of the IEEE"},{"key":"2026040313321947200_ref189","first-page":"461","article-title":"Estimating the dimension of a model,","author":"Schwarz","year":"1978","journal-title":"The annals of statistics"},{"key":"2026040313321947200_ref190","first-page":"8","article-title":"Dirichlet bayesian network scores and the maximum entropy principle,","author":"Scutari","year":"2017","journal-title":"Advanced Methodologies for Bayesian Networks"},{"key":"2026040313321947200_ref191","article-title":"Introduction to graphical mod-elling,","author":"Scutari","year":"2010","journal-title":"arXiv preprint arXiv:1005.1036"},{"key":"2026040313321947200_ref192","first-page":"19 467","article-title":"Data-SUITE: Data-centric identification of in-distribution incongruous examples,","volume":"162","author":"Seedat","year":"2022","journal-title":"Proceedings of the 39th International Conference on Machine Learning"},{"key":"2026040313321947200_ref193","article-title":"Continuous-time modeling of counterfactual outcomes using neural controlled differential equations,","author":"Seedat","year":"2022","journal-title":"arXiv preprint arXiv:2206. 08311"},{"key":"2026040313321947200_ref194","first-page":"22 905","volume-title":"Advances in Neural Information Processing Systems","author":"Seitzer","year":"2021"},{"key":"2026040313321947200_ref195","first-page":"518","article-title":"Contextual Bandits with Latent Confounders: An NMF Approach,","volume":"54","author":"Sen","year":"2017","journal-title":"Proceedings of the 20th International Conference"},{"issue":"2","key":"2026040313321947200_ref196","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1016\/j.jor.2018.05.033","article-title":"Demographics and rates of surgical arthroscopy and postoperative rehabilitative preferences of arthroscopists from the arthroscopy association of north america (aana),","volume":"15","author":"Shah","year":"2018","journal-title":"Journal of orthopaedics"},{"key":"2026040313321947200_ref197","first-page":"3076","article-title":"Estimating individual treatment effect: Generalization bounds and algorithms,","author":"Shalit","year":"2017","journal-title":"International Conference on Machine Learning"},{"key":"2026040313321947200_ref198","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1145\/2764468.2764488","article-title":"Estimating the causal impact of recommendation systems from observational data,","author":"Sharma","year":"2015","journal-title":"Proceedings of the Sixteenth ACM Conference on Economics"},{"key":"2026040313321947200_ref199","first-page":"1","article-title":"Incorporating causality in energy consumption forecasting using deep neural networks,","author":"Sharma","year":"2022","journal-title":"Annals of Operations Research"},{"key":"2026040313321947200_ref200","article-title":"Warm starting bandits with side information from confounded data,","author":"Sharma","year":"2020","journal-title":"arXiv preprint arXiv:2002.08405"},{"issue":"9","key":"2026040313321947200_ref201","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.7326\/M21-0252","article-title":"Postdiagnosis smoking cessation and reduced risk for lung cancer progression and mortality: A prospective cohort study,","volume":"174","author":"Sheikh","year":"2021","journal-title":"Annals of internal medicine"},{"key":"2026040313321947200_ref202","first-page":"1","article-title":"Weakly supervised disentangled generative causal representation learning,","volume":"23","author":"Shen","year":"2022","journal-title":"Journal of Machine Learning Research"},{"key":"2026040313321947200_ref203","first-page":"1","article-title":"Dynamic causal effects evaluation in a\/b testing with a reinforcement learning framework,","author":"Shi","year":"2022","journal-title":"Journal of the American Statistical Association"},{"key":"2026040313321947200_ref204","first-page":"1546","article-title":"Invariant representation learning for treatment effect estimation,","author":"Shi","year":"2021","journal-title":"Uncertainty in Artificial Intelligence"},{"key":"2026040313321947200_ref205","first-page":"1225","article-title":"Directlingam: A direct method for learning a linear non-gaussian structural equation model,","volume":"12","author":"Shimizu","year":"2011","journal-title":"Journal of Machine Learning Research-JMLR"},{"issue":"9789","key":"2026040313321947200_ref206","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1016\/S0140-6736(11)60478-9","article-title":"Vaccine production, distribution, access, and uptake,","volume":"378","author":"Smith","year":"2011","journal-title":"The Lancet"},{"issue":"1","key":"2026040313321947200_ref207","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1177\/089443939100900106","article-title":"An algorithm for fast recovery of sparse causal graphs,","volume":"9","author":"Spirtes","year":"1991","journal-title":"Social science computer review"},{"key":"2026040313321947200_ref208","volume-title":"Causation, prediction, and search","author":"Spirtes","year":"2000"},{"key":"2026040313321947200_ref209","first-page":"688","volume-title":"Conference on Causal Learning and Reasoning","author":"Squires","year":"2022"},{"issue":"11","key":"2026040313321947200_ref210","doi-asserted-by":"crossref","first-page":"1551","DOI":"10.1007\/s40262-023-01310-x","article-title":"Bridging the worlds of pharmacometrics and machine learning,","volume":"62","author":"Stankevi\u010di\u016bt\u0117","year":"2023","journal-title":"Clinical Pharmacokinetics"},{"key":"2026040313321947200_ref211","article-title":"Causal contextual bandits with targeted interventions,","author":"Subramanian","year":"2022","journal-title":"International Conference on Learning Representations"},{"key":"2026040313321947200_ref212","first-page":"1","article-title":"Causal inference by choosing graphs with most plausible markov kernels,","author":"Sun","year":"2006","journal-title":"Ninth International Symposium on Artificial Intelligence and Mathematics"},{"issue":"2","key":"2026040313321947200_ref213","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1093\/bioinformatics\/btz602","article-title":"Non-parametric individual treatment effect estimation for survival data with random forests,","volume":"36","author":"Tabib","year":"2020","journal-title":"Bioinformatics"},{"key":"2026040313321947200_ref214","first-page":"430","volume-title":"Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence","author":"Tennenholtz","year":"2021"},{"key":"2026040313321947200_ref215","first-page":"86","volume-title":"Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence","author":"Teshima","year":"2021"},{"key":"2026040313321947200_ref216","first-page":"376","article-title":"Algorithms for large scale markov blanket discovery.,","volume":"2","author":"Tsamardinos","year":"2003","journal-title":"FLAIRS"},{"key":"2026040313321947200_ref217","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s10994-006-6889-7","article-title":"The max-min hill-climbing bayesian network structure learning algorithm,","volume":"65","author":"Tsamardinos","year":"2006","journal-title":"Machine learning"},{"issue":"5","key":"2026040313321947200_ref218","doi-asserted-by":"crossref","first-page":"194","DOI":"10.3810\/pgm.2011.09.2475","article-title":"Key concepts of clinical trials: A narrative review,","volume":"123","author":"Umscheid","year":"2011","journal-title":"Postgraduate medicine"},{"issue":"1","key":"2026040313321947200_ref219","doi-asserted-by":"crossref","first-page":"5848","DOI":"10.1038\/s41598-022-09775-9","article-title":"Individual treatment effect estimation in the presence of unobserved confounding using proxies: A cohort study in stage iii non-small cell lung cancer,","volume":"12","author":"van Amsterdam","year":"2022","journal-title":"Scientific reports"},{"key":"2026040313321947200_ref220","first-page":"22 221","article-title":"Decaf: Generating fair synthetic data using causally-aware generative networks,","volume":"34","author":"van Breugel","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2026040313321947200_ref221","article-title":"NOFLITE: Learning to predict individual treatment effect distributions,","author":"Vanderschueren","year":"2023","journal-title":"Transactions on Machine Learning Research"},{"key":"2026040313321947200_ref222","first-page":"34 855","article-title":"Accounting for informative sampling when learning to forecast treatment outcomes over time,","volume-title":"Proceedings of the 40th International Conference on Machine Learning","author":"Vanderschueren","year":"2023"},{"key":"2026040313321947200_ref223","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/978-3-319-21852-6_3","article-title":"On the uniform convergence of relative frequencies of events to their probabilities,","author":"Vapnik","year":"2015","journal-title":"Measures of complexity: festschrift for alexey chervonenkis"},{"key":"2026040313321947200_ref224","article-title":"Attention is all you need,","volume":"30","author":"Vaswani","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"2026040313321947200_ref225","author":"Vergano","year":"2020","journal-title":"Clinical ethics recommendations for the allocation of intensive care treatments in exceptional, resource-limited circumstances: The italian perspective"},{"key":"2026040313321947200_ref226","article-title":"Equivalence and synthesis of causal models,","author":"Verma","year":"1990","journal-title":"Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence"},{"key":"2026040313321947200_ref227","article-title":"D\u2019ya like dags? a survey on structure learning and causal discovery,","author":"Vowels","year":"2021","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"2026040313321947200_ref228","article-title":"Proactive pseudo-intervention: Causally informed contrastive learning for interpretable vision models,","author":"Wang","year":"2020","journal-title":"arXiv preprint arXiv:2012.03369"},{"key":"2026040313321947200_ref229","first-page":"21 164","volume-title":"Advances in Neural Information Processing Systems","author":"Wang","year":"2021"},{"key":"2026040313321947200_ref230","first-page":"378","article-title":"Visual commonsense representation learning via causal inference,","author":"Wang","year":"2020","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops"},{"key":"2026040313321947200_ref231","first-page":"3566","article-title":"Ordering-based causal discovery with reinforcement learning,","author":"Wang","year":"2021","journal-title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21)"},{"key":"2026040313321947200_ref232","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1145\/3383313.3412225","article-title":"Causal inference for recommender systems,","author":"Wang","year":"2020","journal-title":"Proceedings of the 14th ACM Conference on Recommender Systems"},{"key":"2026040313321947200_ref233","first-page":"3895","article-title":"Dags with no fears: A closer look at continuous optimization for learning bayesian networks,","volume":"33","author":"Wei","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2026040313321947200_ref234","article-title":"Causal-tgan: Generating tabular data using causal generative adversarial networks,","author":"Wen","year":"2021","journal-title":"arXiv preprint arXiv:2104.10680"},{"key":"2026040313321947200_ref235","first-page":"1351","article-title":"Fast gaussian process based gradient matching for parameter identification in systems of nonlinear odes,","volume-title":"Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics","author":"Wenk","year":"2019"},{"issue":"5","key":"2026040313321947200_ref236","doi-asserted-by":"crossref","first-page":"1731","DOI":"10.1093\/ije\/dyv135","article-title":"Imputation approaches for potential outcomes in causal inference,","volume":"44","author":"Westreich","year":"2015","journal-title":"International journal of epidemiology"},{"issue":"1","key":"2026040313321947200_ref237","first-page":"9956","article-title":"On efficient adjustment in causal graphs,","volume":"21","author":"Witte","year":"2020","journal-title":"The Journal of Machine Learning Research"},{"issue":"1","key":"2026040313321947200_ref238","doi-asserted-by":"crossref","first-page":"8","DOI":"10.3390\/econometrics10010008","article-title":"The impact of covid-19 on airfares\u2014a machine learning counterfactual analysis,","volume":"10","author":"Wozny","year":"2022","journal-title":"Econometrics"},{"key":"2026040313321947200_ref239","first-page":"23","article-title":"On the opportunity of causal learning in recommendation systems: Foundation, estimation, prediction and challenges,","author":"Wu","year":"2022","journal-title":"Proceedings of the International Joint Conference on Artificial Intelligence, Vienna, Austria"},{"key":"2026040313321947200_ref240","article-title":"Pc-fairness: A unified framework for measuring causality-based fairness,","volume":"32","author":"Wu","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"2026040313321947200_ref241","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/201","article-title":"Achieving causal fairness through generative adversarial networks,","author":"Xu","journal-title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"},{"key":"2026040313321947200_ref242","article-title":"Causality learning: A new perspective for interpretable machine learning,","author":"Xu","year":"2020","journal-title":"arXiv preprint arXiv:2006.16789"},{"key":"2026040313321947200_ref243","article-title":"Causal inference q-network: Toward resilient reinforcement learning,","author":"Yang","year":"2021","journal-title":"arXiv preprint arXiv:2102.09677"},{"issue":"1","key":"2026040313321947200_ref244","first-page":"15","article-title":"Machine learning applications in drug repurposing,","volume":"14","author":"Yang","year":"2022","journal-title":"Interdisciplinary"},{"key":"2026040313321947200_ref245","article-title":"Path-specific causal fair prediction via auxiliary graph structure learning,","author":"Yao","year":"2021"},{"key":"2026040313321947200_ref246","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v31i1.10711","article-title":"Personalized donor-recipient matching for organ transplantation,","author":"Yoon","year":"2017","journal-title":"Thirty-First AAAI Conference on Artificial Intelligence"},{"issue":"3","key":"2026040313321947200_ref247","doi-asserted-by":"crossref","first-page":"e0194985","DOI":"10.1371\/journal.pone.0194985","article-title":"Personalized survival predictions via trees of predictors: An application to cardiac transplantation,","volume":"13","author":"Yoon","year":"2018","journal-title":"PloS one"},{"key":"2026040313321947200_ref248","first-page":"7154","article-title":"DAG-GNN: DAG structure learning with graph neural networks,","volume-title":"Proceedings of the 36th International Conference on Machine Learning","author":"Yu","year":"2019"},{"key":"2026040313321947200_ref249","first-page":"12 156","article-title":"Dags with no curl: An efficient dag structure learning approach,","volume":"139","author":"Yu","year":"2021","journal-title":"Proceedings of the 38th International Conference on Machine Learning"},{"issue":"4","key":"2026040313321947200_ref250","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1080\/19466315.2020.1797867","article-title":"Machine learning for clinical trials in the era of covid-19,","volume":"12","author":"Zame","year":"2020","journal-title":"Statistics in Biopharmaceutical Research"},{"issue":"2","key":"2026040313321947200_ref251","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s11023-008-9096-4","article-title":"Detection of unfaithfulness and robust causal inference,","volume":"18","author":"Zhang","year":"2008","journal-title":"Minds and Machines"},{"key":"2026040313321947200_ref252","article-title":"Causal bandits: Online decision-making in endogenous settings,","author":"Zhang","year":"2022","journal-title":"arXiv preprint arXiv: 2211.08649"},{"key":"2026040313321947200_ref253","first-page":"11 012","article-title":"Designing optimal dynamic treatment regimes: A causal reinforcement learning approach,","volume":"119","author":"Zhang","year":"2020","journal-title":"Proceedings of the 37th International Conference on Machine Learning"},{"key":"2026040313321947200_ref254","article-title":"On the identifiability of the post-nonlinear causal model,","author":"Zhang","year":"2012","journal-title":"arXiv preprint arXiv:1205.2599"},{"key":"2026040313321947200_ref255","first-page":"1","article-title":"Cmgan: A generative adversarial network embedded with causal matrix,","author":"Zhang","year":"2022","journal-title":"Applied Intelligence"},{"key":"2026040313321947200_ref256","first-page":"11 270","article-title":"Learning causal representation for training cross-domain pose estimator via generative interventions,","author":"Zhang","year":"2021","journal-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision"},{"key":"2026040313321947200_ref257","first-page":"4158","article-title":"Identifiable energy-based representations: An application to estimating heterogeneous causal effects,","author":"Zhang","year":"2022","journal-title":"International Conference on Artificial"},{"issue":"4","key":"2026040313321947200_ref258","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1002\/cpt.1795","article-title":"Will artificial intelligence for drug discovery impact clinical pharmacology?","volume":"107","author":"Zhavoronkov","year":"2020","journal-title":"Clinical Pharmacology & Therapeutics"},{"key":"2026040313321947200_ref259","article-title":"Dags with no tears: Continuous optimization for structure learning,","volume":"31","author":"Zheng","year":"2018","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2026040313321947200_ref260","first-page":"3414","article-title":"Learning sparse nonparametric dags,","author":"Zheng","year":"2020","journal-title":"International Conference"},{"key":"2026040313321947200_ref261","article-title":"On the opportunity of causal deep generative models: A survey and future directions,","author":"Zhou","year":"2023","journal-title":"arXiv preprint arXiv:2301.12351"},{"key":"2026040313321947200_ref262","article-title":"Causal discovery with reinforcement learning,","author":"Zhu","year":"2020","journal-title":"International Conference on Learning Representations"},{"key":"2026040313321947200_ref263","article-title":"Causal deep reinforcement learning using observational data,","author":"Zhu","year":"2022","journal-title":"arXiv preprint arXiv:2211.15355"}],"container-title":["Foundations and Trends\u00ae in Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/ftsig\/article-pdf\/18\/3\/200\/10973433\/2000000123en.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/www.emerald.com\/ftsig\/article-pdf\/18\/3\/200\/10973433\/2000000123en.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T17:32:52Z","timestamp":1775237572000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.emerald.com\/ftsig\/article\/18\/3\/200\/1324613\/Causal-Deep-Learning-Encouraging-Impact-on-Real"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,1]]},"references-count":263,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,8,1]]}},"URL":"https:\/\/doi.org\/10.1561\/2000000123","relation":{},"ISSN":["1932-8346","1932-8354"],"issn-type":[{"value":"1932-8346","type":"print"},{"value":"1932-8354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,1]]}}}