{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T01:11:54Z","timestamp":1771549914861,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":56,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T00:00:00Z","timestamp":1665964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The National Research Foundation Singapore and DSO National Laboratories","award":["AISG2-RP-2020-016"],"award-info":[{"award-number":["AISG2-RP-2020-016"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,17]]},"DOI":"10.1145\/3511808.3557230","type":"proceedings-article","created":{"date-parts":[[2022,10,16]],"date-time":"2022-10-16T01:29:57Z","timestamp":1665883797000},"page":"1975-1985","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Adaptive Multi-Source Causal Inference from Observational Data"],"prefix":"10.1145","author":[{"given":"Thanh Vinh","family":"Vo","sequence":"first","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}]},{"given":"Pengfei","family":"Wei","sequence":"additional","affiliation":[{"name":"AI Lab Speech &amp; Audio Bytedance, Singapore, Singapore"}]},{"given":"Trong Nghia","family":"Hoang","sequence":"additional","affiliation":[{"name":"Washington State University, Pullman, WA, USA"}]},{"given":"Tze Yun","family":"Leong","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}]}],"member":"320","published-online":{"date-parts":[[2022,10,17]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"6293","article-title":"Multi-task causal learning with gaussian processes","volume":"33","author":"Aglietti Virginia","year":"2020","unstructured":"Virginia Aglietti , Theodoros Damoulas , Mauricio \u00c1lvarez , and Javier Gonz\u00e1lez . 2020 . Multi-task causal learning with gaussian processes . Advances in Neural Information Processing Systems , Vol. 33 (2020), 6293 -- 6304 . Virginia Aglietti, Theodoros Damoulas, Mauricio \u00c1lvarez, and Javier Gonz\u00e1lez. 2020. Multi-task causal learning with gaussian processes. Advances in Neural Information Processing Systems, Vol. 33 (2020), 6293--6304.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_2_1","unstructured":"Ahmed M Alaa and Mihaela van der Schaar. 2017. Bayesian inference of individualized treatment effects using multi-task gaussian processes. In Advances in Neural Information Processing Systems. 3424--3432.  Ahmed M Alaa and Mihaela van der Schaar. 2017. Bayesian inference of individualized treatment effects using multi-task gaussian processes. In Advances in Neural Information Processing Systems. 3424--3432."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1214\/09-AOS689"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2697055"},{"key":"e_1_3_2_1_5_1","unstructured":"Elias Bareinboim Sanghack Lee Vasant Honavar and Judea Pearl. 2013. Transportability from multiple environments with limited experiments. In Advances in Neural Information Processing Systems. 136--144.  Elias Bareinboim Sanghack Lee Vasant Honavar and Judea Pearl. 2013. Transportability from multiple environments with limited experiments. In Advances in Neural Information Processing Systems. 136--144."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v27i1.8692"},{"key":"e_1_3_2_1_7_1","unstructured":"Elias Bareinboim and Judea Pearl. 2014. Transportability from multiple environments with limited experiments: Completeness results. In Advances in neural information processing systems. 280--288.  Elias Bareinboim and Judea Pearl. 2014. Transportability from multiple environments with limited experiments: Completeness results. In Advances in neural information processing systems. 280--288."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1510507113"},{"key":"e_1_3_2_1_9_1","volume-title":"International Conference on Machine Learning. PMLR, 884--895","author":"Bica Ioana","year":"2020","unstructured":"Ioana Bica , Ahmed Alaa , and Mihaela Van Der Schaar . 2020 a. Time series deconfounder: Estimating treatment effects over time in the presence of hidden confounders . In International Conference on Machine Learning. PMLR, 884--895 . Ioana Bica, Ahmed Alaa, and Mihaela Van Der Schaar. 2020a. Time series deconfounder: Estimating treatment effects over time in the presence of hidden confounders. In International Conference on Machine Learning. PMLR, 884--895."},{"key":"e_1_3_2_1_10_1","volume-title":"International Conference on Learning Representations.","author":"Bica Ioana","unstructured":"Ioana Bica , Ahmed M Alaa , James Jordon , and Mihaela van der Schaar. 2020 b. Estimating counterfactual treatment outcomes over time through adversarially balanced representations . In International Conference on Learning Representations. Ioana Bica, Ahmed M Alaa, James Jordon, and Mihaela van der Schaar. 2020 b. Estimating counterfactual treatment outcomes over time through adversarially balanced representations. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_11_1","volume-title":"CausalML: Python Package for Causal Machine Learning. arxiv","author":"Chen Huigang","year":"2002","unstructured":"Huigang Chen , Totte Harinen , Jeong-Yoon Lee , Mike Yung , and Zhenyu Zhao . 2020. CausalML: Python Package for Causal Machine Learning. arxiv : 2002 .11631 [cs.CY] Huigang Chen, Totte Harinen, Jeong-Yoon Lee, Mike Yung, and Zhenyu Zhao. 2020. CausalML: Python Package for Causal Machine Learning. arxiv: 2002.11631 [cs.CY]"},{"key":"e_1_3_2_1_12_1","volume-title":"NPCI: Non-parametrics for causal inference.","author":"Dorie Vincent","year":"2016","unstructured":"Vincent Dorie . 2016 . NPCI: Non-parametrics for causal inference. Vincent Dorie. 2016. NPCI: Non-parametrics for causal inference."},{"key":"e_1_3_2_1_13_1","volume-title":"Modeling heterogeneous treatment effects in survey experiments with Bayesian additive regression trees. Public opinion quarterly","author":"Green Donald P","year":"2012","unstructured":"Donald P Green and Holger L Kern . 2012. Modeling heterogeneous treatment effects in survey experiments with Bayesian additive regression trees. Public opinion quarterly , Vol. 76 , 3 ( 2012 ), 491--511. Donald P Green and Holger L Kern. 2012. Modeling heterogeneous treatment effects in survey experiments with Bayesian additive regression trees. Public opinion quarterly, Vol. 76, 3 (2012), 491--511."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1017\/pan.2017.15"},{"key":"e_1_3_2_1_15_1","volume-title":"International Conference on Machine Learning. 1414--1423","author":"Hartford Jason","year":"2017","unstructured":"Jason Hartford , Greg Lewis , Kevin Leyton-Brown , and Matt Taddy . 2017 . Deep IV: A flexible approach for counterfactual prediction . In International Conference on Machine Learning. 1414--1423 . Jason Hartford, Greg Lewis, Kevin Leyton-Brown, and Matt Taddy. 2017. Deep IV: A flexible approach for counterfactual prediction. In International Conference on Machine Learning. 1414--1423."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10742-016-0159-3"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1198\/jcgs.2010.08162"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1214\/12-AOAS593"},{"key":"e_1_3_2_1_19_1","volume-title":"The 22nd International Conference on Artificial Intelligence and Statistics. 2281--2290","author":"Kallus Nathan","year":"2019","unstructured":"Nathan Kallus , Xiaojie Mao , and Angela Zhou . 2019 . Interval estimation of individual-level causal effects under unobserved confounding . In The 22nd International Conference on Artificial Intelligence and Statistics. 2281--2290 . Nathan Kallus, Xiaojie Mao, and Angela Zhou. 2019. Interval estimation of individual-level causal effects under unobserved confounding. In The 22nd International Conference on Artificial Intelligence and Statistics. 2281--2290."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02293554"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1804597116"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/ast066"},{"key":"e_1_3_2_1_23_1","volume-title":"Proceedings of the 34th AAAI Conference on Artificial Intelligence. AAAI Press","author":"Lee S.","unstructured":"S. Lee , J. Correa , and E. Bareinboim . 2020. Generalized Transportability: Synthesis of Experiments from Heterogeneous Domains . In Proceedings of the 34th AAAI Conference on Artificial Intelligence. AAAI Press , New York, NY. S. Lee, J. Correa, and E. Bareinboim. 2020. Generalized Transportability: Synthesis of Experiments from Heterogeneous Domains. In Proceedings of the 34th AAAI Conference on Artificial Intelligence. AAAI Press, New York, NY."},{"key":"e_1_3_2_1_24_1","unstructured":"Christos Louizos Uri Shalit Joris M Mooij David Sontag Richard Zemel and Max Welling. 2017. Causal effect inference with deep latent-variable models. In Advances in Neural Information Processing Systems. 6446--6456.  Christos Louizos Uri Shalit Joris M Mooij David Sontag Richard Zemel and Max Welling. 2017. Causal effect inference with deep latent-variable models. In Advances in Neural Information Processing Systems. 6446--6456."},{"key":"e_1_3_2_1_25_1","volume-title":"Deconfounding reinforcement learning in observational settings. arXiv preprint arXiv:1812.10576","author":"Lu Chaochao","year":"2018","unstructured":"Chaochao Lu , Bernhard Sch\u00f6lkopf , and Jos\u00e9 Miguel Hern\u00e1ndez-Lobato . 2018. Deconfounding reinforcement learning in observational settings. arXiv preprint arXiv:1812.10576 ( 2018 ). Chaochao Lu, Bernhard Sch\u00f6lkopf, and Jos\u00e9 Miguel Hern\u00e1ndez-Lobato. 2018. Deconfounding reinforcement learning in observational settings. arXiv preprint arXiv:1812.10576 (2018)."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287564"},{"key":"e_1_3_2_1_27_1","unstructured":"Sara Magliacane Thijs van Ommen Tom Claassen Stephan Bongers Philip Versteeg and Joris M Mooij. 2018. Domain adaptation by using causal inference to predict invariant conditional distributions. In Advances in Neural Information Processing Systems. 10846--10856.  Sara Magliacane Thijs van Ommen Tom Claassen Stephan Bongers Philip Versteeg and Joris M Mooij. 2018. Domain adaptation by using causal inference to predict invariant conditional distributions. In Advances in Neural Information Processing Systems. 10846--10856."},{"key":"e_1_3_2_1_28_1","unstructured":"Microsoft Research. 2019. EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation. https:\/\/github.com\/microsoft\/EconML. Version 0.x.  Microsoft Research. 2019. EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation. https:\/\/github.com\/microsoft\/EconML. Version 0.x."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.2307\/2648118"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asaa076"},{"key":"e_1_3_2_1_31_1","volume-title":"International Conference on Machine Learning. PMLR, 4932--4941","author":"Oprescu Miruna","year":"2019","unstructured":"Miruna Oprescu , Vasilis Syrgkanis , and Zhiwei Steven Wu . 2019 . Orthogonal random forest for causal inference . In International Conference on Machine Learning. PMLR, 4932--4941 . Miruna Oprescu, Vasilis Syrgkanis, and Zhiwei Steven Wu. 2019. Orthogonal random forest for causal inference. In International Conference on Machine Learning. PMLR, 4932--4941."},{"key":"e_1_3_2_1_32_1","volume-title":"International Conference on Machine Learning. PMLR, 4942--4950","author":"Osama Muhammad","year":"2019","unstructured":"Muhammad Osama , Dave Zachariah , and Thomas B Sch\u00f6n . 2019 . Inferring heterogeneous causal effects in presence of spatial confounding . In International Conference on Machine Learning. PMLR, 4942--4950 . Muhammad Osama, Dave Zachariah, and Thomas B Sch\u00f6n. 2019. Inferring heterogeneous causal effects in presence of spatial confounding. In International Conference on Machine Learning. PMLR, 4942--4950."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/82.4.669"},{"key":"e_1_3_2_1_35_1","volume-title":"Causality: models, reasoning and inference","author":"Pearl Judea","unstructured":"Judea Pearl . 2000. Causality: models, reasoning and inference . Vol. 29 . Springer . Judea Pearl. 2000. Causality: models, reasoning and inference. Vol. 29. Springer."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","unstructured":"Judea Pearl and Elias Bareinboim. 2014. External validity: From do-calculus to transportability across populations. Statist. Sci. (2014) 579--595.  Judea Pearl and Elias Bareinboim. 2014. External validity: From do-calculus to transportability across populations. Statist. Sci. (2014) 579--595.","DOI":"10.1214\/14-STS486"},{"key":"e_1_3_2_1_37_1","volume-title":"Some methods for heterogeneous treatment effect estimation in high dimensions. Statistics in medicine","author":"Powers Scott","year":"2018","unstructured":"Scott Powers , Junyang Qian , Kenneth Jung , Alejandro Schuler , Nigam H Shah , Trevor Hastie , and Robert Tibshirani . 2018. Some methods for heterogeneous treatment effect estimation in high dimensions. Statistics in medicine , Vol. 37 , 11 ( 2018 ), 1767--1787. Scott Powers, Junyang Qian, Kenneth Jung, Alejandro Schuler, Nigam H Shah, Trevor Hastie, and Robert Tibshirani. 2018. Some methods for heterogeneous treatment effect estimation in high dimensions. Statistics in medicine, Vol. 37, 11 (2018), 1767--1787."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3269267"},{"key":"e_1_3_2_1_39_1","volume-title":"Generalized conditional domain adaptation: A causal perspective with low-rank translators","author":"Ren Chuan-Xian","year":"2018","unstructured":"Chuan-Xian Ren , Xiao-Lin Xu , and Hong Yan . 2018. Generalized conditional domain adaptation: A causal perspective with low-rank translators . IEEE transactions on cybernetics, Vol. 50 , 2 ( 2018 ), 821--834. Chuan-Xian Ren, Xiao-Lin Xu, and Hong Yan. 2018. Generalized conditional domain adaptation: A causal perspective with low-rank translators. IEEE transactions on cybernetics, Vol. 50, 2 (2018), 821--834."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1353\/rhe.2008.0010"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1161\/01.HYP.24.6.779"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.5555\/3291125.3291161"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/70.1.41"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1037\/h0037350"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214504000001880"},{"key":"e_1_3_2_1_46_1","volume-title":"Perfect match: A simple method for learning representations for counterfactual inference with neural networks. arXiv preprint arXiv:1810.00656","author":"Schwab Patrick","year":"2018","unstructured":"Patrick Schwab , Lorenz Linhardt , and Walter Karlen . 2018. Perfect match: A simple method for learning representations for counterfactual inference with neural networks. arXiv preprint arXiv:1810.00656 ( 2018 ). Patrick Schwab, Lorenz Linhardt, and Walter Karlen. 2018. Perfect match: A simple method for learning representations for counterfactual inference with neural networks. arXiv preprint arXiv:1810.00656 (2018)."},{"key":"e_1_3_2_1_47_1","volume-title":"Proceedings of the 34th International Conference on Machine Learning-Volume 70","author":"Shalit Uri","year":"2017","unstructured":"Uri Shalit , Fredrik D Johansson , and David Sontag . 2017 . Estimating individual treatment effect: generalization bounds and algorithms . In Proceedings of the 34th International Conference on Machine Learning-Volume 70 . JMLR.org, 3076--3085. Uri Shalit, Fredrik D Johansson, and David Sontag. 2017. Estimating individual treatment effect: generalization bounds and algorithms. In Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR.org, 3076--3085."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1080\/07350015.2016.1172013"},{"key":"e_1_3_2_1_49_1","volume-title":"International Conference on Machine Learning. PMLR, 9458--9469","author":"Teshima Takeshi","year":"2020","unstructured":"Takeshi Teshima , Issei Sato , and Masashi Sugiyama . 2020 . Few-shot domain adaptation by causal mechanism transfer . In International Conference on Machine Learning. PMLR, 9458--9469 . Takeshi Teshima, Issei Sato, and Masashi Sugiyama. 2020. Few-shot domain adaptation by causal mechanism transfer. In International Conference on Machine Learning. PMLR, 9458--9469."},{"key":"e_1_3_2_1_50_1","unstructured":"Victor Veitch Yixin Wang and David Blei. 2019. Using embeddings to correct for unobserved confounding in networks. In Advances in Neural Information Processing Systems. 13792--13802.  Victor Veitch Yixin Wang and David Blei. 2019. Using embeddings to correct for unobserved confounding in networks. In Advances in Neural Information Processing Systems. 13792--13802."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1319839"},{"key":"e_1_3_2_1_52_1","volume-title":"International Conference on Machine Learning. PMLR, 10313--10323","author":"Witty Sam","year":"2020","unstructured":"Sam Witty , Kenta Takatsu , David Jensen , and Vikash Mansinghka . 2020 . Causal inference using Gaussian processes with structured latent confounders . In International Conference on Machine Learning. PMLR, 10313--10323 . Sam Witty, Kenta Takatsu, David Jensen, and Vikash Mansinghka. 2020. Causal inference using Gaussian processes with structured latent confounders. In International Conference on Machine Learning. PMLR, 10313--10323."},{"key":"e_1_3_2_1_53_1","unstructured":"Liuyi Yao Sheng Li Yaliang Li Mengdi Huai Jing Gao and Aidong Zhang. 2018. Representation learning for treatment effect estimation from observational data. In Advances in Neural Information Processing Systems. 2633--2643.  Liuyi Yao Sheng Li Yaliang Li Mengdi Huai Jing Gao and Aidong Zhang. 2018. Representation learning for treatment effect estimation from observational data. In Advances in Neural Information Processing Systems. 2633--2643."},{"key":"e_1_3_2_1_54_1","volume-title":"International Conference on Learning Representations.","author":"Yoon Jinsung","unstructured":"Jinsung Yoon , James Jordon , and Mihaela van der Schaar. 2018. GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets . In International Conference on Learning Representations. Jinsung Yoon, James Jordon, and Mihaela van der Schaar. 2018. GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/186"},{"key":"e_1_3_2_1_56_1","volume-title":"Learning overlapping representations for the estimation of individualized treatment effects. arXiv preprint arXiv:2001.04754","author":"Zhang Yao","year":"2020","unstructured":"Yao Zhang , Alexis Bellot , and Mihaela van der Schaar . 2020. Learning overlapping representations for the estimation of individualized treatment effects. arXiv preprint arXiv:2001.04754 ( 2020 ). Yao Zhang, Alexis Bellot, and Mihaela van der Schaar. 2020. Learning overlapping representations for the estimation of individualized treatment effects. arXiv preprint arXiv:2001.04754 (2020)."}],"event":{"name":"CIKM '22: The 31st ACM International Conference on Information and Knowledge Management","location":"Atlanta GA USA","acronym":"CIKM '22","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557230","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3511808.3557230","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:49:07Z","timestamp":1750182547000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557230"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,17]]},"references-count":56,"alternative-id":["10.1145\/3511808.3557230","10.1145\/3511808"],"URL":"https:\/\/doi.org\/10.1145\/3511808.3557230","relation":{},"subject":[],"published":{"date-parts":[[2022,10,17]]},"assertion":[{"value":"2022-10-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}