arXiv 30 Sep 2018 · Econometrics · 10 citations (OpenAlex)
arXiv:1810.00283 · PDF · DOI · OpenAlex · Extracted main text
We provide new results for nonparametric identification, estimation, and inference of causal effects using `proxy controls': observables that are noisy but informative proxies for unobserved confounding factors. Our analysis applies to cross-sectional settings but is particularly well-suited to panel models. Our identification results motivate a simple and `well-posed' nonparametric estimator. We derive convergence rates for the estimator and construct uniform confidence bands with asymptotically correct size. In panel settings, our methods provide a novel approach to the difficult problem of identification with non-separable, general heterogeneity and fixed $T$. In panels, observations from different periods serve as proxies for unobserved heterogeneity and our key identifying assumptions follow from restrictions on the serial dependence structure. We apply our methods to two empirical settings. We estimate consumer demand counterfactuals using panel data and we estimate causal effects of grade retention on cognitive performance.
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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 | Fruehwirth, Jane Cooley, Navarro, Salvador, & Takahashi, Yuya (2016) How the Timing of Grade Retention Affects Outcomes: Identification and Estimation of Time-Varying Treatment Effects | 1.000 | 9 | 3 | 100% |
| 2 | Miao, Wang, Geng, Zhi, & Tchetgen, Eric J. Tchetgen (2018) Identifying causal effects with proxy variables of an unmeasured confounder | 0.971 | 12 | 4 | 92% |
| 3 | Belloni, Alexandre, Chernozhukov, Victor, Chetverikov, Denis, & Kato… (2015) Some new asymptotic theory for least squares series: Pointwise and uniform results | 0.928 | 10 | 3 | 80% |
| 4 | Hu, Yingyao, & Schennach, Susanne M (2008) Instrumental Variable Treatment of Nonclassical Measurement Error Models | 0.874 | 5 | 2 | 100% |
| 5 | Pearl, Judea (2009) Causality: Models, Reasoning, and Inference (Second Edition) | 0.737 | 5 | 3 | 40% |
| 6 | Chen, Xiaohong, & Pouzo, Demian (2015) Sieve Wald and QLR Inferences on Semi/Nonparametric Conditional Moment Models | 0.644 | 2 | 2 | 100% |
| 7 | Griliches, Zvi (1977) Estimating the Returns to Schooling: Some Econometric Problems | 0.644 | 2 | 2 | 100% |
| 8 | Shi, Xu, Miao, Wang, Nelson, Jennifer C., & Tchetgen, Eric J. Tchetgen (2020) Multiply robust causal inference with double-negative control adjustment for categorical unmeasured confounding | 0.644 | 2 | 2 | 100% |
| 9 | Newey, Whitney K., & Powell, James L (2003) Instrumental Variable Estimation of Nonparametric Models | 0.585 | 3 | 1 | 100% |
| 10 | Pollard, David (2001) A User's Guide to Measure Theoretic Probability | 0.511 | 2 | 2 | 50% |
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