Debiased Bayesian inference for average treatment effects
This work addresses causal inference challenges for researchers and practitioners in fields like healthcare or economics, offering a competitive method that is incremental in improving Bayesian approaches.
The paper tackled the problem of Bayesian inference for average treatment effects from observational data, which suffers from missing counterfactuals and selection bias, by proposing a data-driven modification to an arbitrary prior based on the propensity score to correct for first-order posterior bias, resulting in significant improvement in estimation accuracy and uncertainty quantification compared to unmodified methods.
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.