Generalized Bayes for Causal Inference

TL;DR

This paper introduces a novel generalized Bayesian framework for causal inference that avoids explicit likelihood modeling, enabling flexible, robust, and calibrated uncertainty quantification for various causal estimands.

Contribution

It proposes a likelihood-free Bayesian approach for causal effects, integrating with existing causal machine learning methods and providing valid uncertainty even with slow nuisance estimator convergence.

Findings

Framework yields calibrated uncertainty in causal effect estimation.

Converges to oracle posteriors for Neyman-orthogonal losses.

Robust to nuisance estimation errors, applicable to multiple causal estimands.

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-orthogonal losses, we show that the generalized posteriors converge to their oracle counterparts and remain robust to first-stage nuisance estimation error. With calibration, we thus obtain valid frequentist uncertainty even when nuisance estimators converge at slower-than-parametric rates. Empirically, we demonstrate that our proposed framework offers causal effect estimation with calibrated uncertainty across several causal inference settings. To the best of our knowledge, this is the first flexible framework for constructing generalized Bayesian posteriors for causal machine learning.