arXiv 10 Sep 2019 · Statistics — Machine Learning · 1 citations (OpenAlex)
arXiv:1909.05244 · PDF · DOI · OpenAlex · Extracted main text
We propose a semiparametric test to evaluate (i) whether different instruments induce subpopulations of compliers with the same observable characteristics on average, and (ii) whether compliers have observable characteristics that are the same as the full population on average. The test is a flexible robustness check for the external validity of instruments. We use it to reinterpret the difference in LATE estimates that Angrist and Evans (1998) obtain when using different instrumental variables. To justify the test, we characterize the doubly robust moment for Abadie (2003)'s class of complier parameters, and we analyze a machine learning update to $\kappa$ weighting.
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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 | 0.976 | 14 | 7 | 93% |
| 2 | Angrist, J. D. and W. N. Evans (1998) Children and their parents’ labor supply: Evidence from exogenous variation in family size | 0.950 | 7 | 5 | 86% |
| 3 | Angrist, J. D. and I. Fernández-Val (2013) ExtrapoLATE-ing: External validity and overidentification in the LATE framework | 0.894 | 7 | 3 | 71% |
| 4 | Tan, Z (2006) Regression and weighting methods for causal inference using instrumental variables | 0.894 | 7 | 3 | 71% |
| 5 | Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W… (2018) Double/debiased machine learning for treatment and structural parameters | 0.794 | 6 | 4 | 50% |
| 6 | Belloni, A., V. Chernozhukov, I. Fernández-Val, and C. Hansen (2017) Program evaluation and causal inference with high-dimensional data | 0.737 | 5 | 4 | 40% |
| 7 | Angrist, J. D., G. W. Imbens, and D. B. Rubin (1996) Identification of causal effects using instrumental variables | 0.737 | 3 | 2 | 100% |
| 8 | Frölich, M (2007) Nonparametric IV estimation of local average treatment effects with covariates | 0.737 | 3 | 2 | 100% |
| 9 | Robins, J. M. and A. Rotnitzky (1995) Semiparametric efficiency in multivariate regression models with missing data | 0.644 | 3 | 2 | 67% |
| 10 | Newey, W. K (1994) The asymptotic variance of semiparametric estimators | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 43 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Identification and Estimation in a Class of Potential Outcomes Models | 0.405 | 1 | 1 |