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Machine learning approaches for counterfactual reasoning have to deal with both unobserved outcomes and distributional differences due to non-random treatment administration. Unsupervised domain adaptation (UDA) addresses similar issues; one has to deal with unobserved outcomes\u2014the labels of the target domain\u2014and distributional differences between source and target domain. We propose Adversarial Distribution Balancing for Counterfactual Reasoning (ADBCR), which directly uses potential outcome estimates of the counterfactuals to remove spurious causal relations. We show that ADBCR outcompetes state-of-the-art methods on three benchmark datasets, and demonstrate that ADBCR\u2019s performance can be further improved if unlabeled validation data are included in the training procedure to better adapt the model to the validation domain.<\/jats:p>","DOI":"10.1007\/s10994-026-07034-w","type":"journal-article","created":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T07:15:23Z","timestamp":1776237323000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adversarial Distribution Balancing for Counterfactual Reasoning"],"prefix":"10.1007","volume":"115","author":[{"given":"Stefan","family":"Schrod","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fabian","family":"Sinz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Altenbuchinger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,15]]},"reference":[{"key":"7034_CR1","unstructured":"Assaad, S., Zeng, S., Tao, C., Datta, S., Mehta, N., Henao, R., Li, F., & Carin, L. 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