Pedro H. C. Sant'Anna
arXiv 10 Apr 2016 · Statistics — Methodology · 3 citations (OpenAlex)
arXiv:1604.02642 · PDF · DOI · OpenAlex · Extracted main text
In a unified framework, we provide estimators and confidence bands for a variety of treatment effects when the outcome of interest, typically a duration, is subjected to right censoring. Our methodology accommodates average, distributional, and quantile treatment effects under different identifying assumptions including unconfoundedness, local treatment effects, and nonlinear differences-in-differences. The proposed estimators are easy to implement, have close-form representation, are fully data-driven upon estimation of nuisance parameters, and do not rely on parametric distributional assumptions, shape restrictions, or on restricting the potential treatment effect heterogeneity across different subpopulations. These treatment effects results are obtained as a consequence of more general results on two-step Kaplan-Meier estimators that are of independent interest: we provide conditions for applying (i) uniform law of large numbers, (ii) functional central limit theorems, and (iii) we prove the validity of the ordinary nonparametric bootstrap in a two-step estimation procedure where the outcome of interest may be randomly censored.
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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 | Frandsen, B. R (2015) b): Treatment Effects With Censoring and Endogeneity | 1.000 | 15 | 4 | 100% |
| 2 | Abadie, A (2003) Semiparametric instrumental variable estimation of treatment response models | 1.000 | 5 | 3 | 100% |
| 3 | Athey, S. and G. W. Imbens (2006) Identification and inference in nonlinear difference in differences models | 1.000 | 5 | 3 | 100% |
| 4 | Frölich, M. and B. Melly (2013) Unconditional Quantile Treatment Effects Under Endogeneity | 1.000 | 5 | 3 | 100% |
| 5 | Hirano, K., G. W. Imbens, and G. Ridder (2003) Efficient estimation of average treatment effects using the estimated propensity score | 0.874 | 9 | 4 | 67% |
| 6 | Rosenbaum, P. R. and D. B. Rubin (1983) The central role of the propensity score in observational studies for causal effects | 0.874 | 5 | 2 | 100% |
| 7 | Donald, S. G. and Y.-C. Hsu (2014) Estimation and inference for distribution functions and quantile functions in treatment effect models | 0.843 | 4 | 3 | 75% |
| 8 | Stute, W (1993) Consistent estimation under random censorship when covariables are present | 0.843 | 3 | 3 | 100% |
| 9 | Stute, W. and J.-L. Wang (1993) The strong law under random censorship | 0.843 | 3 | 3 | 100% |
| 10 | Chen, X., O. Linton, and I. Van Keilegom (2003) Estimation of semiparametric models when the criterion function is not smooth | 0.811 | 4 | 2 | 100% |
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