# Debiased Bayesian inference for average treatment effects

arXiv: https://arxiv.org/abs/1909.12078 · HTML: https://mlsanity.com/paper/1909.12078/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2019.

## Built on by (most cited first, 7 of 7)

- [Estimating Average Treatment Effects via Orthogonal Regularization](https://mlsanity.com/paper/2101.08490/) (2021)
- [Double Robust Bayesian Inference on Average Treatment Effects](https://mlsanity.com/paper/2211.16298/) (2022)
- [Bayesian Semi-supervised Inference via a Debiased Modeling Approach](https://mlsanity.com/paper/2509.17385/) (2025)
- [Bayesian Semiparametric Causal Inference: Targeted Doubly Robust Estimation of Treatment Effects](https://mlsanity.com/paper/2511.15904/) (2025)
- [MEC: Machine-Learning-Assisted Generalized Entropy Calibration for Semi-Supervised Mean Estimation](https://mlsanity.com/paper/2604.05446/) (2026)
- [Robust Orthogonal Machine Learning of Treatment Effects](https://mlsanity.com/paper/2103.11869/) (2021)
- [Average Adjusted Association: Efficient Estimation with High Dimensional Confounders](https://mlsanity.com/paper/2205.14048/) (2022)

## Abstract (arXiv)

Bayesian approaches have become increasingly popular in causal inference problems due to their conceptual simplicity, excellent performance and in-built uncertainty quantification ('posterior credible sets'). We investigate Bayesian inference for average treatment effects from observational data, which is a challenging problem due to the missing counterfactuals and selection bias. Working in the standard potential outcomes framework, we propose a data-driven modification to an arbitrary (nonparametric) prior based on the propensity score that corrects for the first-order posterior bias, thereby improving performance. We illustrate our method for Gaussian process (GP) priors using (semi-)synthetic data. Our experiments demonstrate significant improvement in both estimation accuracy and uncertainty quantification compared to the unmodified GP, rendering our approach highly competitive with the state-of-the-art.