Christoph Breunig, Ruixuan Liu, Zhengfei Yu
arXiv 5 Dec 2024 · Econometrics
arXiv:2412.04605 · PDF · DOI · OpenAlex · Extracted main text
This paper studies semiparametric Bayesian inference for the average treatment effect on the treated (ATT) within the difference-in-differences (DiD) research design. We propose two new Bayesian methods with frequentist validity. The first one places a standard Gaussian process prior on the conditional mean function of the control group. The second method is a double robust Bayesian procedure that adjusts the prior distribution of the conditional mean function and subsequently corrects the posterior distribution of the resulting ATT. We prove new semiparametric Bernstein-von Mises (BvM) theorems for both proposals. Monte Carlo simulations and an empirical application demonstrate that the proposed Bayesian DiD methods exhibit strong finite-sample performance compared to existing frequentist methods. We also present extensions of the canonical DiD approach, incorporating both the staggered design and the repeated cross-sectional design.
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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 | Sant’Anna and Zhao (2020) Doubly robust difference-in-differences estimators | 1.000 | 12 | 5 | 100% |
| 2 | Abadie (2005) Semiparametric difference-in-differences estimators | 1.000 | 6 | 5 | 100% |
| 3 | Heckman, Ichimura, and Todd (1997) Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme | 0.928 | 4 | 4 | 100% |
| 4 | Breunig, Liu, and Yu (2025) Double robust Bayesian inference on average treatment effects self | 0.894 | 21 | 9 | 71% |
| 5 | Rassmusen and Williams (2006) Gaussian processes for machine learning | 0.874 | 6 | 3 | 67% |
| 6 | Kleijn and van der Vaart (2006) Misspecification in infinite-dimensional Bayesian statistics | 0.843 | 4 | 4 | 75% |
| 7 | Yiu, Fong, Holmes, and Rousseau (2023) Semiparametric posterior corrections | 0.843 | 4 | 3 | 75% |
| 8 | Ray and van der Vaart (2020) Semiparametric Bayesian causal inference | 0.794 | 16 | 7 | 50% |
| 9 | Ghosal and Van der Vaart (2017) Fundamentals of nonparametric Bayesian inference | 0.754 | 7 | 5 | 43% |
| 10 | Hahn (1998) On the role of the propensity score in efficient semiparametric estimation of average treatment effects | 0.737 | 3 | 3 | 67% |
Showing the top 10 of 50 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Bayesian Double Machine Learning for Causal Inference | 0.511 | 2 | 1 |
| 2 | Robust Semiparametric Inference for Bayesian Additive Regression Trees | 0.511 | 2 | 2 |
| 3 | Semiparametric Bayesian Inference for a Conditional Moment Equality Model | 0.405 | 1 | 1 |
| 4 | Debiased Bayesian Inference for High-dimensional Regression Models | 0.405 | 1 | 1 |