# Generalized Bayes for Causal Inference

arXiv: https://arxiv.org/abs/2603.03035 · HTML: https://mlsanity.com/paper/2603.03035/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 42 references that are in the map.
- First posted on arXiv 3 Mar 2026.

## Builds on (most cited first, 22 of 22)

- [Quasi-Oracle Estimation of Heterogeneous Treatment Effects](https://mlsanity.com/paper/1712.04912/) (2017)
- [A General Framework for Updating Belief Distributions](https://mlsanity.com/paper/1306.6430/) (2013)
- [Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes](https://mlsanity.com/paper/1704.02801/) (2017)
- [Orthogonal Statistical Learning](https://mlsanity.com/paper/1901.09036/) (2019)
- [Semiparametric doubly robust targeted double machine learning: a review](https://mlsanity.com/paper/2203.06469/) (2022)
- [Causal machine learning for predicting treatment outcomes](https://mlsanity.com/paper/2410.08770/) (2024)
- [Treatment effect estimation with disentangled latent factors](https://mlsanity.com/paper/2001.10652/) (2020)
- [Robust Generalised Bayesian Inference for Intractable Likelihoods](https://mlsanity.com/paper/2104.07359/) (2021)
- [Calibrating general posterior credible regions](https://mlsanity.com/paper/1509.00922/) (2015)
- [Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with β-Divergences](https://mlsanity.com/paper/1806.02261/) (2018)
- [Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation](https://mlsanity.com/paper/2305.15208/) (2023)
- [An MCMC Approach to Classical Estimation](https://mlsanity.com/paper/2301.07782/) (2023)
- [Frequentist Consistency of Generalized Variational Inference](https://mlsanity.com/paper/1912.04946/) (2019)
- [The How and Why of Bayesian Nonparametric Causal Inference](https://mlsanity.com/paper/2111.03897/) (2021)
- [Double Robust Bayesian Inference on Average Treatment Effects](https://mlsanity.com/paper/2211.16298/) (2022)
- [Quasi-Bayesian Dual Instrumental Variable Regression](https://mlsanity.com/paper/2106.08750/) (2021)
- [A Bayesian view of doubly robust causal inference](https://mlsanity.com/paper/1701.04093/) (2017)
- [Causal inference under mis-specification: adjustment based on the propensity score](https://mlsanity.com/paper/2201.12831/) (2022)
- [Bayesian causal inference with some invalid instrumental variables](https://mlsanity.com/paper/2006.07663/) (2020)
- [Ensemble Doubly Robust Bayesian Inference via Regression Synthesis](https://mlsanity.com/paper/2409.06288/) (2024)
- [Bayesian Doubly Robust Causal Inference via Posterior Coupling](https://mlsanity.com/paper/2506.04868/) (2025)
- [Bayesian Semiparametric Causal Inference: Targeted Doubly Robust Estimation of Treatment Effects](https://mlsanity.com/paper/2511.15904/) (2025)

## Built on by (most cited first, 1 of 1)

- [Calibrated Inference for the Conditional Average Treatment Effect in the Few-Placebo Regime via Gaussian Processes](https://mlsanity.com/paper/2605.27473/) (2026)

## Abstract (arXiv)

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.