Gözde Sert, Abhishek Chakrabortty, Anirban Bhattacharya
arXiv 19 Nov 2025 · Statistics — Methodology
arXiv:2511.15904 · PDF · DOI · OpenAlex · Extracted main text
We propose a semiparametric Bayesian methodology for estimating the average treatment effect (ATE) within the potential outcomes framework using observational data with high-dimensional nuisance parameters. Our method introduces a Bayesian debiasing procedure that corrects for bias arising from nuisance estimation and employs a targeted modeling strategy based on summary statistics rather than the full data. These summary statistics are identified in a debiased manner, enabling the estimation of nuisance bias via weighted observables and facilitating hierarchical learning of the ATE. By combining debiasing with sample splitting, our approach separates nuisance estimation from inference on the target parameter, reducing sensitivity to nuisance model specification. We establish that, under mild conditions, the marginal posterior for the ATE satisfies a Bernstein-von Mises theorem when both nuisance models are correctly specified and remains consistent and robust when only one is correct, achieving Bayesian double robustness. This ensures asymptotic efficiency and frequentist validity. Extensive simulations confirm the theoretical results, demonstrating accurate point estimation and credible intervals with nominal coverage, even in high-dimensional settings. The proposed framework can also be extended to other causal estimands, and its key principles offer a general foundation for advancing Bayesian semiparametric inference more broadly.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Kolyan Ray and Aad van der Vaart (2020) Semiparametric Bayesian causal inference | 1.000 | 12 | 3 | 100% |
| 2 | Christoph Breunig, Ruixuan Liu, and Zhengfei Yu (2025) Double robust Bayesian inference on average treatment effects | 1.000 | 8 | 3 | 100% |
| 3 | Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2018) Double/debiased machine learning for treatment and structural parameters | 1.000 | 5 | 3 | 100% |
| 4 | Jinyong Hahn (1998) On the role of the propensity score in efficient semiparametric estimation of average treatment effects | 0.928 | 4 | 3 | 100% |
| 5 | Andrew Yiu, Edwin Fong, Chris Holmes, and Judith Rousseau (2025) Semiparametric posterior corrections | 0.874 | 7 | 2 | 100% |
| 6 | Kolyan Ray and Botond Szabó (2019) Debiased Bayesian inference for average treatment effects | 0.874 | 5 | 2 | 100% |
| 7 | Yu Luo, Daniel J Graham, and Emma J McCoy (2023) Semiparametric Bayesian doubly robust causal estimation | 0.843 | 3 | 3 | 100% |
| 8 | Paul Rosenbaum and Donald Rubin (1984) Reducing bias in observational studies using subclassification on the propensity score | 0.843 | 3 | 3 | 100% |
| 9 | Anastasios Tsiatis (2007) Semiparametric Theory and Missing Data | 0.843 | 3 | 3 | 100% |
| 10 | Heejung Bang and James M Robins (2005) Doubly robust estimation in missing data and causal inference models | 0.811 | 4 | 2 | 100% |
Showing the top 10 of 39 scored citations.