Difang Huang, Jiti Gao, Tatsushi Oka
arXiv 17 Jun 2022 · Econometrics · publishedEconometric Reviews (2025) · 2 citations (OpenAlex)
arXiv:2206.08503 · PDF · DOI · OpenAlex · Extracted main text
We propose a semiparametric method to estimate the average treatment effect under the assumption of unconfoundedness given observational data. Our estimation method alleviates misspecification issues of the propensity score function by estimating the single-index link function involved through Hermite polynomials. Our approach is computationally tractable and allows for moderately large dimension covariates. We provide the large sample properties of the estimator and show its validity. Also, the average treatment effect estimator achieves the parametric rate and asymptotic normality. Our extensive Monte Carlo study shows that the proposed estimator is valid in finite samples. Applying our method to maternal smoking and infant health, we find that conventional estimates of smoking's impact on birth weight may be biased due to propensity score misspecification, and our analysis of job training programs reveals earnings effects that are more precisely estimated than in prior work. These applications demonstrate how addressing model misspecification can substantively affect our understanding of key policy-relevant treatment effects.
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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 | Dong, C., J. Gao, and B. Peng (2019) Series estimation for single-index models under constraints | 1.000 | 14 | 3 | 100% |
| 2 | Liu, J., Y. Ma, and L. Wang (2018) An alternative robust estimator of average treatment effect in causal inference | 1.000 | 9 | 6 | 100% |
| 3 | Sun, Y., K. X. Yan, and Q. Li (2021) Estimation of average treatment effect based on a semiparametric propensity score | 1.000 | 7 | 4 | 100% |
| 4 | Hirano, K., G. Imbens, and G. Ridder (2003) Efficient estimation of average treatment effects using the estimated propensity score | 1.000 | 5 | 4 | 100% |
| 5 | Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W… (2018) Double/debiased machine learning for treatment and structural parameters | 0.928 | 4 | 3 | 100% |
| 6 | Chen, X (2007) Chapter 76 large sample sieve estimation of semi-nonparametric models | 0.843 | 3 | 3 | 100% |
| 7 | Tan, Z (2010) Bounded, efficient and doubly robust estimation with inverse weighting | 0.843 | 3 | 3 | 100% |
| 8 | Abadie, A. and G. Imbens (2006) Large sample properties of matching estimators for average treatment effects | 0.843 | 3 | 3 | 100% |
| 9 | Abadie, A. and G. Imbens (2011) Bias-corrected matching estimators for average treatment effects | 0.843 | 3 | 3 | 100% |
| 10 | Lane, P. W. and J. A. Nelder (1982) Analysis of covariance and standardization as instances of prediction | 0.843 | 3 | 3 | 100% |
Showing the top 10 of 84 scored citations.