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Poster
Debiased Bayesian inference for average treatment effects
Kolyan Ray · Botond Szabo

Tue Dec 10 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #182

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.

Author Information

Kolyan Ray (King's College London)
Botond Szabo (Leiden University)

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