arXiv 7 Jun 2023 · Econometrics · publishedBiometrika (2024)
arXiv:2306.04177 · PDF · DOI · OpenAlex · Extracted main text
We explore how much knowing a parametric restriction on propensity scores improves semiparametric efficiency bounds in the potential outcome framework. For stratified propensity scores, considered as a parametric model, we derive explicit formulas for the efficiency gain from knowing how the covariate space is split. Based on these, we find that the efficiency gain decreases as the partition of the stratification becomes finer. For general parametric models, where it is hard to obtain explicit representations of efficiency bounds, we propose a novel framework that enables us to see whether knowing a parametric model is valuable in terms of efficiency even when it is high-dimensional. In addition to the intuitive fact that knowing the parametric model does not help much if it is sufficiently flexible, we discover that the efficiency gain can be nearly zero even though the parametric assumption significantly restricts the space of possible propensity scores.
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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 | Cattaneo, Matias D (2010) Efficient semiparametric estimation of multi-valued treatment effects under ignorability | 1.000 | 5 | 3 | 100% |
| 2 | Chen, Xiaohong, Hong, Han, Tarozzi, Alessandro (2008) Semiparametric efficiency in GMM models with auxiliary data | 1.000 | 5 | 3 | 100% |
| 3 | Hahn, Jinyong (1998) On the role of the propensity score in efficient semiparametric estimation of average treatment effects | 0.969 | 11 | 4 | 91% |
| 4 | Lee, Ying-Ying (2018) Efficient propensity score regression estimators of multivalued treatment effects for the treated | 0.969 | 11 | 4 | 91% |
| 5 | Bickel, PJ, Klaassen, CAJ, Ritov, Y, Wellner, JA (1993) Efficient and adaptive estimation for semiparametric models | 0.737 | 3 | 3 | 67% |
| 6 | Firpo, Sergio (2007) Efficient semiparametric estimation of quantile treatment effects | 0.644 | 2 | 2 | 100% |
| 7 | (2004) A note on the role of the propensity score for estimating average treatment effects | 0.644 | 2 | 2 | 100% |
| 8 | Hirano, Keisuke, Imbens, Guido W, Ridder, Geert (2003) Efficient estimation of average treatment effects using the estimated propensity score | 0.511 | 2 | 1 | 100% |
| 9 | Bai, Yuehao, Liu, Jizhou, Shaikh, Azeem M, Tabord-Meehan, Max (2024) On the efficiency of finely stratified experiments | 0.405 | 1 | 1 | 100% |
| 10 | Bugni, Federico A, Canay, Ivan A, Shaikh, Azeem M (2019) Inference under covariate-adaptive randomization with multiple treatments | 0.405 | 1 | 1 | 100% |
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