Doubly robust identification of treatment effects from multiple environments
Keywords: treatment effect, confounding, heterogenous data, causality, causal inference, unobserved variables, post-treatment variables, collider bias
Abstract: Practical and ethical constraints often dictate the use of observational data for causal inference, particularly in medicine and social sciences. Yet, observational datasets are prone to confounding, potentially compromising the validity of conclusions. While adjusting for all available covariates is a common corrective strategy, this approach can introduce bias, especially when post-treatment variables are present or some variables remain unobserved—a frequent scenario in practice. Avoiding this bias often requires detailed knowledge of the underlying causal graph, a challenging and often impractical prerequisite. In this work, we propose RAMEN, an algorithm that tackles this challenge by leveraging the heterogeneity of multiple data sources without the need to know the complete causal graph. Notably, RAMEN achieves *doubly robust identification*: we identify the treatment effect if either the causal parents of the treatment or those of the outcome are observed. Empirical evaluations across synthetic, semi-synthetic, and real-world datasets show that our approach significantly outperforms existing methods.
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Primary Area: other topics in machine learning (i.e., none of the above)
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Submission Number: 8459
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