{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T06:15:24Z","timestamp":1774678524214,"version":"3.50.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T00:00:00Z","timestamp":1667347200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T00:00:00Z","timestamp":1667347200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"JSPS KAKENHI","award":["21J14882"],"award-info":[{"award-number":["21J14882"]}]},{"name":"JSPS KAKENHI","award":["20H04244"],"award-info":[{"award-number":["20H04244"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Treatment effect estimation is a fundamental problem in various domains for effective decision making. While many studies assume that observational data include all the confounding variables, we cannot practically guarantee that observational data include such confounding variables, and there might be confounding variables that are not included in observational data, referred to as hidden confounding variables. Recently, variational autencoder\u00a0(VAE)\u00a0based methods have been successfully applied to treatment effect estimation problem. However, although they can recover a large class of latent variable models, they do not give the correct treatment effect, even when they achieve an optimal solution due to the nature of VAE loss function. We propose an efficient VAE-based method that employs information theory to estimate treatment effect and combines it with a matching technique. To the best of our knowledge, this is the first work that gives the correct treatment effect given an optimal solution using VAE-based methods. Experiments on a semi-real dataset and synthetic dataset demonstrate that the proposed method mitigates VAE problems and observational bias effectively, even under hidden confounding variables, and outperforms strong baseline methods.<\/jats:p>","DOI":"10.1007\/s10994-022-06246-0","type":"journal-article","created":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T20:23:37Z","timestamp":1667420617000},"page":"1799-1817","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["InfoCEVAE: treatment effect estimation with hidden confounding variables matching"],"prefix":"10.1007","volume":"113","author":[{"given":"Shonosuke","family":"Harada","sequence":"first","affiliation":[]},{"given":"Hisashi","family":"Kashima","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,2]]},"reference":[{"issue":"1","key":"6246_CR1","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1111\/j.1468-0262.2006.00655.x","volume":"74","author":"A Abadie","year":"2006","unstructured":"Abadie, A., & Imbens, G. W. (2006). Large sample properties of matching estimators for average treatment effects. Econometrica, 74(1), 235\u2013267.","journal-title":"Econometrica"},{"issue":"1","key":"6246_CR2","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5\u201332.","journal-title":"Machine Learning"},{"key":"6246_CR3","unstructured":"Cai, Z, Kuroki, M. (2008). On identifying total effects in the presence of latent variables and selection bias. In Proceedings of the twenty-fourth conference on uncertainty in artificial intelligence (UAI), p 62\u201369."},{"issue":"1","key":"6246_CR4","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1214\/09-AOAS285","volume":"4","author":"HA Chipman","year":"2010","unstructured":"Chipman, H. A., George, E. I., McCulloch, R. E., et al. (2010). Bart: Bayesian additive regression trees. The Annals of Applied Statistics, 4(1), 266\u2013298.","journal-title":"The Annals of Applied Statistics"},{"key":"6246_CR5","unstructured":"Clevert, D.A., Unterthiner T., Hochreiter, S. (2015). Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289."},{"issue":"6","key":"6246_CR6","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1002\/cpt.515","volume":"100","author":"HG Eichler","year":"2016","unstructured":"Eichler, H. G., Bloechl-Daum, B., Bauer, P., et al. (2016). Threshold-crossing: A useful way to establish the counterfactual in clinical trials? Clinical Pharmacology & Therapeutics, 100(6), 699\u2013712.","journal-title":"Clinical Pharmacology & Therapeutics"},{"key":"6246_CR7","doi-asserted-by":"crossref","unstructured":"Guo, R. Li, J. Liu, H. (2020). Learning individual causal effects from networked observational data. In Proceedings of the 13th international conference on web search and data mining (WSDM), pp 232\u2013240.","DOI":"10.1145\/3336191.3371816"},{"key":"6246_CR8","doi-asserted-by":"crossref","unstructured":"Harada, S. Kashima, H. (2020). Counterfactual propagation for semi-supervised individual treatment effect estimation. In Joint European conference on machine learning and knowledge discovery in databases, Springer, pp 542\u2013558.","DOI":"10.1007\/978-3-030-67658-2_31"},{"key":"6246_CR9","doi-asserted-by":"crossref","unstructured":"Harada, S. Kashima, H. (2021). Graphite: Estimating individual effects of graph-structured treatments. In Proceedings of the 30th ACM international conference on information & knowledge management\u00a0(CIKM), pp 659\u2013668.","DOI":"10.1145\/3459637.3482349"},{"issue":"1","key":"6246_CR10","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1198\/jcgs.2010.08162","volume":"20","author":"JL Hill","year":"2011","unstructured":"Hill, J. L. (2011). Bayesian nonparametric modeling for causal inference. Journal of Computational and Graphical Statistics, 20(1), 217\u2013240.","journal-title":"Journal of Computational and Graphical Statistics"},{"key":"6246_CR11","unstructured":"Johansson, F. Shalit U, Sontag D. (2016). Learning representations for counterfactual inference. In Proceedings of the 33rd international conference on machine learning (ICML), pp 3020\u20133029."},{"key":"6246_CR12","unstructured":"Kingma, D.P., Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114."},{"issue":"4","key":"6246_CR13","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1017\/pan.2019.11","volume":"27","author":"G King","year":"2019","unstructured":"King, G., & Nielsen, R. (2019). Why propensity scores should not be used for matching. Political Analysis, 27(4), 435\u2013454.","journal-title":"Political Analysis"},{"key":"6246_CR14","first-page":"604","volume":"76","author":"RJ LaLonde","year":"1986","unstructured":"LaLonde, R. J. (1986). Evaluating the econometric evaluations of training programs with experimental data. The American Economic Review, 76, 604\u2013620.","journal-title":"The American Economic Review"},{"key":"6246_CR15","doi-asserted-by":"crossref","unstructured":"Liu, M.Y., Breuel, T., Kautz, J. (2017). Unsupervised image-to-image translation networks. In textit Advances in Neural Information Processing Systems\u00a0(NeurIPS).","DOI":"10.1007\/978-3-319-70139-4"},{"key":"6246_CR16","unstructured":"Liu, Q. Allamanis, M. Brockschmidt, M. et al. (2018). Constrained graph variational autoencoders for molecule design. In Advances in Neural Information Processing Systems\u00a0(NeurIPS)."},{"key":"6246_CR17","unstructured":"Louizos, C. Shalit U. Mooij J. et al. (2017). Causal effect inference with deep latent-variable models. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"6246_CR18","unstructured":"Miao, Y., Yu, L., Blunsom, P. (2016). Neural variational inference for text processing. In Proceedings of the 33th international conference on machine learning (ICML), pp 1727\u20131736."},{"issue":"1","key":"6246_CR19","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1093\/biomet\/70.1.41","volume":"70","author":"PR Rosenbaum","year":"1983","unstructured":"Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41\u201355.","journal-title":"Biometrika"},{"issue":"1","key":"6246_CR20","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1080\/00031305.1985.10479383","volume":"39","author":"PR Rosenbaum","year":"1985","unstructured":"Rosenbaum, P. R., & Rubin, D. B. (1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician, 39(1), 33\u201338.","journal-title":"The American Statistician"},{"key":"6246_CR21","volume-title":"Modern epidemiology","author":"KJ Rothman","year":"2008","unstructured":"Rothman, K. J., Greenland, S., Lash, T. L., et al. (2008). Modern epidemiology. Philadelphia: Wolters Kluwer Health\/Lippincott Williams & Wilkins."},{"key":"6246_CR22","doi-asserted-by":"publisher","first-page":"159","DOI":"10.2307\/2529684","volume":"29","author":"DB Rubin","year":"1973","unstructured":"Rubin, D. B. (1973). Matching to remove bias in observational studies. Biometrics, 29, 159\u2013183.","journal-title":"Biometrics"},{"key":"6246_CR23","unstructured":"Schwab, P. Linhardt, L. Karlen, W. (2018). Perfect match: A simple method for learning representations for counterfactual inference with neural networks. arXiv preprint arXiv:1810.00656"},{"key":"6246_CR24","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1146\/annurev.polisci.11.060606.135444","volume":"12","author":"JS Sekhon","year":"2009","unstructured":"Sekhon, J. S. (2009). Opiates for the matches: Matching methods for causal inference. Annual Review of Political Science, 12, 487\u2013508.","journal-title":"Annual Review of Political Science"},{"key":"6246_CR25","unstructured":"Shalit, U., Johansson, F.D. Sontag, D. (2017). Estimating individual treatment effect: Generalization bounds and algorithms. In Proceedings of the 34th international conference on machine learning\u00a0(ICML), pp 3076\u20133085."},{"key":"6246_CR26","unstructured":"Tran, D. Ranganath, R, Blei, D. M. (2015). The variational gaussian process. arXiv preprint arXiv:1511.06499."},{"key":"6246_CR27","unstructured":"Xu, L. Kanagawa, H. Gretton, A. (2021). Deep proxy causal learning and its application to confounded bandit policy evaluation. In Advances in Neural Information Processing Systems\u00a0(NeurIPS)."},{"key":"6246_CR28","unstructured":"Yao, L. Li, S. Li, Y. et al. (2018). Representation learning for treatment effect estimation from observational data. In Advances in Neural Information Processing Systems\u00a0(NeurIPS)."},{"key":"6246_CR29","unstructured":"Yoon, J. Jordon, J. van der Schaar, M. (2018). Ganite: Estimation of individualized treatment effects using generative adversarial nets. In Proceedigns of the 6th international conference on learning representations (ICLR)."},{"key":"6246_CR30","doi-asserted-by":"crossref","unstructured":"Zhang, W. Liu, L. Li, J. (2021). Treatment effect estimation with disentangled latent factors. In Proceedings of the AAAI conference on artificial intelligence\u00a0(AAAI), pp 10923\u201310930.","DOI":"10.1609\/aaai.v35i12.17304"},{"key":"6246_CR31","unstructured":"Zhao, S. Heffernan, N. (2017). Estimating individual treatment effect from educational studies with residual counterfactual networks. In Proceedings of the 10th international conference on educational data mining\u00a0(EDM)."},{"key":"6246_CR32","doi-asserted-by":"crossref","unstructured":"Zhao, S. Song, J. Ermon, S. (2019). Infovae: Balancing learning and inference in variational autoencoders. In Proceedings of the AAAI conference on artificial intelligence\u00a0(AAAI), pp 5885\u20135892.","DOI":"10.1609\/aaai.v33i01.33015885"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-022-06246-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10994-022-06246-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-022-06246-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T17:11:45Z","timestamp":1711645905000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10994-022-06246-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,2]]},"references-count":32,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["6246"],"URL":"https:\/\/doi.org\/10.1007\/s10994-022-06246-0","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,2]]},"assertion":[{"value":"1 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 August 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 September 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 November 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}}]}}