arXiv 28 Apr 2022 · Econometrics
arXiv:2204.13815 · PDF · DOI · OpenAlex · Extracted main text
We present new results for nonparametric identification of causal effects using noisy proxies for unobserved confounders. Our approach builds on the results of \citet{Hu2008} who tackle the problem of general measurement error. We call this the `triple proxy' approach because it requires three proxies that are jointly independent conditional on unobservables. We consider three different choices for the third proxy: it may be an outcome, a vector of treatments, or a collection of auxiliary variables. We compare to an alternative identification strategy introduced by \citet{Miao2018a} in which causal effects are identified using two conditionally independent proxies. We refer to this as the `double proxy' approach. The triple proxy approach identifies objects that are not identified by the double proxy approach, including some that capture the variation in average treatment effects between strata of the unobservables. Moreover, the conditional independence assumptions in the double and triple proxy approaches are non-nested.
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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 | Deaner, Ben (2021) Proxy Controls and Panel Data self | 1.000 | 5 | 4 | 100% |
| 2 | Hu, Yingyao, & Schennach, Susanne M (2008) Instrumental Variable Treatment of Nonclassical Measurement Error Models | 0.935 | 11 | 6 | 82% |
| 3 | Freyberger, Joachim (2021) Normalizations and misspecification in skill formation models | 0.874 | 5 | 2 | 100% |
| 4 | Miao, Wang, Geng, Zhi, & Tchetgen, Eric J. Tchetgen (2018) Identifying causal effects with proxy variables of an unmeasured confounder | 0.737 | 3 | 2 | 100% |
| 5 | Pearl, Judea (2009) Causality: Models, Reasoning, and Inference (Second Edition) | 0.644 | 2 | 2 | 100% |
| 6 | Rokkanen, Miikka AT (2015) Exam schools, ability, and the effects of affirmative action: Latent factor extrapolation in the regression discontinuity design | 0.644 | 2 | 2 | 100% |
| 7 | Ai, Chunrong, & Chen, Xiaohong (2003) Efficient estimation of models with conditional moment restrictions containing unknown functions | 0.405 | 1 | 1 | 100% |
| 8 | Cunha, Flavio, Heckman, James, & Schennach, Susanne (2010) Estimating the Technology of Cognitive and Noncognitive Skill Formation | 0.405 | 1 | 1 | 100% |
| 9 | Fruehwirth, Jane Cooley, Navarro, Salvador, & Takahashi, Yuya (2016) How the Timing of Grade Retention Affects Outcomes: Identification and Estimation of Time-Varying Treatment Effects | 0.405 | 1 | 1 | 100% |
| 10 | Griliches, Zvi, & Mason, William M (1972) Education, Income, and Ability | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 14 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Proxy Controls and Panel Data | 0.511 | 2 | 1 |
| 2 | Estimating Treatment Effects in Panel Data Without Parallel Trends | 0.405 | 1 | 1 |