arXiv 29 Apr 2024 · Econometrics
arXiv:2404.19144 · PDF · DOI · OpenAlex · Extracted main text
I propose a locally robust semiparametric framework for estimating causal effects using the popular examiner IV design, in the presence of many examiners and possibly many covariates relative to the sample size. The key ingredient of this approach is an orthogonal moment function that is robust to biases and local misspecification from the first step estimation of the examiner IV. I derive the orthogonal moment function and show that it delivers multiple robustness where the outcome model or at least one of the first step components is misspecified but the estimating equation remains valid. The proposed framework not only allows for estimation of the examiner IV in the presence of many examiners and many covariates relative to sample size, using a wide range of nonparametric and machine learning techniques including LASSO, Dantzig, neural networks and random forests, but also delivers root-n consistent estimation of the parameter of interest under mild assumptions.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Kolesár, Michal (2013) Cowles Foundation Yale University Version 1.4, September 30, 2013 | 1.000 | 20 | 3 | 100% |
| 2 | Frandsen, Brigham, Lefgren, Lars, Leslie, Emily (2023) Judging judge fixed effects | 1.000 | 7 | 4 | 100% |
| 3 | Chernozhukov, Victor, Chetverikov, Denis, Demirer, Mert, Duflo, Esth… (2018) Double/debiased machine learning for treatment and structural parameters | 0.941 | 6 | 4 | 83% |
| 4 | Chernozhukov, Victor, Newey, Whitney K, Singh, Rahul (2022) Debiased machine learning of global and local parameters using regularized Riesz representers | 0.928 | 5 | 3 | 80% |
| 5 | Chernozhukov, Victor, Escanciano, Juan Carlos, Ichimura, Hidehiko, N… (2022) Locally robust semiparametric estimation | 0.883 | 45 | 5 | 69% |
| 6 | Ichimura, Hidehiko, Newey, Whitney K (2022) The influence function of semiparametric estimators | 0.883 | 16 | 4 | 69% |
| 7 | Chernozhukov, Victor, Newey, Whitney K, Singh, Rahul (2022) Automatic debiased machine learning of causal and structural effects | 0.843 | 4 | 3 | 75% |
| 8 | Kling, Jeffrey R (2006) Incarceration Length, Employment, and Earnings | 0.811 | 4 | 2 | 100% |
| 9 | Jochmans, Koen (2023) Many (Weak) Judges in Judge-Leniency Designs | 0.811 | 4 | 2 | 100% |
| 10 | Newey, Whitney K (1994) The asymptotic variance of semiparametric estimators | 0.737 | 5 | 2 | 60% |
Showing the top 10 of 40 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
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| 1 | A Sharp Test for the Judge Leniency Design | 0.405 | 1 | 1 |