[2603.03035] Generalized Bayes for Causal Inference

Statistics > Machine Learning arXiv:2603.03035 (stat) [Submitted on 3 Mar 2026] Title:Generalized Bayes for Causal Inference Authors:Emil Javurek, Dennis Frauen, Yuxin Wang, Stefan Feuerriegel View a PDF of the paper titled Generalized Bayes for Causal Inference, by Emil Javurek and 3 other authors View PDF HTML (experimental) Abstract:Uncertainty quantification is central to many applications of causal machine learning, yet principled Bayesian inference for causal effects remains challenging. Standard Bayesian approaches typically require specifying a probabilistic model for the data-generating process, including high-dimensional nuisance components such as propensity scores and outcome regressions. Standard posteriors are thus vulnerable to strong modeling choices, including complex prior elicitation. In this paper, we propose a generalized Bayesian framework for causal inference. Our framework avoids explicit likelihood modeling; instead, we place priors directly on the causal estimands and update these using an identification-driven loss function, which yields generalized posteriors for causal effects. As a result, our framework turns existing loss-based causal estimators into estimators with full uncertainty quantification. Our framework is flexible and applicable to a broad range of causal estimands (e.g., ATE, CATE). Further, our framework can be applied on top of state-of-the-art causal machine learning pipelines (e.g., Neyman-orthogonal meta-learners). For Neyman-...