arXiv 8 Jul 2020 · Econometrics · publishedJournal of Business and Economic Statistics (2021) · 16 citations (OpenAlex)
arXiv:2007.04346 · PDF · DOI · OpenAlex · Extracted main text
This paper develops an empirical balancing approach for the estimation of treatment effects under two-sided noncompliance using a binary conditionally independent instrumental variable. The method weighs both treatment and outcome information with inverse probabilities to produce exact finite sample balance across instrument level groups. It is free of functional form assumptions on the outcome or the treatment selection step. By tailoring the loss function for the instrument propensity scores, the resulting treatment effect estimates exhibit both low bias and a reduced variance in finite samples compared to conventional inverse probability weighting methods. The estimator is automatically weight normalized and has similar bias properties compared to conventional two-stage least squares estimation under constant causal effects for the compliers. We provide conditions for asymptotic normality and semiparametric efficiency and demonstrate how to utilize additional information about the treatment selection step for bias reduction in finite samples. The method can be easily combined with regularization or other statistical learning approaches to deal with a high-dimensional number of observed confounding variables. Monte Carlo simulations suggest that the theoretical advantages translate well to finite samples. The method is illustrated in an empirical example.
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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 | Abadie, A (2003) Semiparametric Instrumental Variable Estimation of Treatment Response Models | 1.000 | 9 | 3 | 100% |
| 2 | Imai, K. and Ratkovic, M (2014) Covariate Balancing Propensity Score | 1.000 | 7 | 4 | 100% |
| 3 | Zhao, Q (2019) Covariate Balancing Propensity Score by Tailored Loss Functions | 0.971 | 12 | 6 | 92% |
| 4 | Frölich, M (2007) Nonparametric IV Estimation of Local Average Treatment Effects with Covariates | 0.961 | 9 | 4 | 89% |
| 5 | Donald, S. G., Hsu, Y.-C., and Lieli, R. P (2014) Testing the Unconfoundedness Assumption via Inverse Probability Weighted Estimators of (L)ATT | 0.928 | 10 | 4 | 80% |
| 6 | Donald, S. G., Hsu, Y.-C., and Lieli, R. P (2014) Inverse Probability Weighted Estimation of Local Average Treatment Effects: A Higher Order MSE Expansion | 0.928 | 4 | 3 | 100% |
| 7 | Hirano, K., Imbens, G. W., and Ridder, G (2003) Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score | 0.855 | 8 | 3 | 62% |
| 8 | Rubin, D. B (2007) The Design versus the Analysis of Observational Studies for Causal Effects: Parallels with the Design of Randomized Trials | 0.843 | 3 | 3 | 100% |
| 9 | Athey, S., Imbens, G. W., and Wager, S (2018) Approximate Residual Balancing: Debiased Inference of Average Treatment Effects in High Dimensions | 0.644 | 2 | 2 | 100% |
| 10 | Heiler, P. and Kazak, E. (forthcoming (2020) Valid Inference for Treatment Effect Parameters under Irregular Identification and Many Extreme Propensity Scores self | 0.644 | 2 | 2 | 100% |
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