arXiv 1 Nov 2018 · Econometrics · 2 citations (OpenAlex)
arXiv:1811.00667 · PDF · DOI · OpenAlex · Extracted main text
I develop a new identification strategy for treatment effects when noisy measurements of unobserved confounding factors are available. I use proxy variables to construct a random variable conditional on which treatment variables become exogenous. The key idea is that, under appropriate conditions, there exists a one-to-one mapping between the distribution of unobserved confounding factors and the distribution of proxies. To ensure sufficient variation in the constructed control variable, I use an additional variable, termed excluded variable, which satisfies certain exclusion restrictions and relevance conditions. I establish asymptotic distributional results for semiparametric and flexible parametric estimators of causal parameters. I illustrate empirical relevance and usefulness of my results by estimating causal effects of attending selective college on earnings.
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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 | Dale, S. B. and A. B. Krueger (2002) Estimating the Payoff to Attending a More Selective College: An Application of Selection on Observables and Unobservables | 0.928 | 4 | 3 | 100% |
| 2 | Hu, Y. and S. M. Schennach (2008) Instrumental Variable Treatment of Nonclassical Measurement Error Models | 0.909 | 8 | 3 | 75% |
| 3 | Blundell, R. W. and J. L. Powell (2003) Endogeneity in Nonparametric and Semiparametric Regression Models, in | 0.811 | 4 | 2 | 100% |
| 4 | Miao, W., Z. Geng, and E. J. Tchetgen Tchetgen (2018) Identifying Causal Effects with Proxy Variables of an Unmeasured Confounder | 0.737 | 4 | 3 | 50% |
| 5 | Deaner, B (2021) Proxy Controls and Panel Data, Working Paper | 0.737 | 3 | 3 | 67% |
| 6 | Imbens, G. W. and W. K. Newey (2009) Identification and Estimation of Triangular Simultaneous Equations models without Additivity | 0.644 | 4 | 1 | 100% |
| 7 | Chernozhukov, V., W. Newey, and R. Singh (2022) Automatic Debiased Machine Learning of Causal and Structural Effects | 0.644 | 3 | 2 | 67% |
| 8 | Altonji, J. G. and R. L. Matzkin (2005) Cross Section and Panel Data Estimators for Nonseparable Models with Endogenous Regressors | 0.644 | 2 | 2 | 100% |
| 9 | Arkhangelsky, D. and G. W. Imbens (2019) The Role of the Propensity Score in Fixed Effect Models, Working Paper | 0.644 | 2 | 2 | 100% |
| 10 | Hahn, J. and G. Ridder (2013) Asymptotic Variance of Semiparametric Estimators with Generated Regressors | 0.644 | 2 | 2 | 100% |
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