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- W2976998341 abstract "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." @default.
- W2976998341 created "2019-10-03" @default.
- W2976998341 creator A5051724436 @default.
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- W2976998341 date "2019-09-26" @default.
- W2976998341 modified "2023-10-16" @default.
- W2976998341 title "Debiased Bayesian inference for average treatment effects" @default.
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