Chunrong Ai, Lukang Huang, Zheng Zhang
arXiv 16 Jul 2018 · Econometrics
arXiv:1807.05678 · PDF · DOI · OpenAlex · Extracted main text
Wang and Tchetgen Tchetgen (2017) studied identification and estimation of the average treatment effect when some confounders are unmeasured. Under their identification condition, they showed that the semiparametric efficient influence function depends on five unknown functionals. They proposed to parameterize all functionals and estimate the average treatment effect from the efficient influence function by replacing the unknown functionals with estimated functionals. They established that their estimator is consistent when certain functionals are correctly specified and attains the semiparametric efficiency bound when all functionals are correctly specified. In applications, it is likely that those functionals could all be misspecified. Consequently their estimator could be inconsistent or consistent but not efficient. This paper presents an alternative estimator that does not require parameterization of any of the functionals. We establish that the proposed estimator is always consistent and always attains the semiparametric efficiency bound. A simple and intuitive estimator of the asymptotic variance is presented, and a small scale simulation study reveals that the proposed estimation outperforms the existing alternatives in finite samples.
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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 | Wang and Tchetgen Tchetgen (2017) Bounded, efficient and multiply robust estimation of average treatment effects using instrumental variables | 0.981 | 18 | 7 | 94% |
| 2 | Hirano, Imbens, and Ridder (2003) Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score | 0.644 | 2 | 2 | 100% |
| 3 | Abadie, Angrist, and Imbens (2002) Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings | 0.405 | 1 | 1 | 100% |
| 4 | Abadie (2003) Semiparametric instrumental variable estimation of treatment response models | 0.405 | 1 | 1 | 100% |
| 5 | Cheng, Small, Tan, and Have (2009) Efficient nonparametric estimation of causal effects in randomized trials with noncompliance | 0.405 | 1 | 1 | 100% |
| 6 | Imbens and Angrist (1994) Identification and Estimation of Local Average Treatment Effects | 0.405 | 1 | 1 | 100% |
| 7 | Angrist, Imbens, and Rubin (1996) Identification of Causal Effects Using Instrumental Variables (Disc: P456-472) | 0.405 | 1 | 1 | 100% |
| 8 | Ogburn, Rotnitzky, and Robins (2015) Doubly robust estimation of the local average treatment effect curve | 0.405 | 1 | 1 | 100% |
| 9 | Tan (2006) Regression and Weighting Methods for Causal Inference Using Instrumental Variables | 0.405 | 1 | 1 | 100% |
| 10 | Chan, Yam, and Zhang (2016) Globally efficient non-parametric inference of average treatment effects by empirical balancing calibration weighting self | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 35 scored citations.