arXiv 26 Jun 2022 · Econometrics
arXiv:2206.12919 · PDF · DOI · OpenAlex · Extracted main text
Causal inference is difficult in the presence of unobserved confounders. We introduce the instrumented common confounding (ICC) approach to (nonparametrically) identify causal effects with instruments, which are exogenous only conditional on some unobserved common confounders. The ICC approach is most useful in rich observational data with multiple sources of unobserved confounding, where instruments are at most exogenous conditional on some unobserved common confounders. Suitable examples of this setting are various identification problems in the social sciences, nonlinear dynamic panels, and problems with multiple endogenous confounders. The ICC identifying assumptions are closely related to those in mixture models, negative control and IV. Compared to mixture models [Bonhomme et al., 2016], we require less conditionally independent variables and do not need to model the unobserved confounder. Compared to negative control [Cui et al., 2020], we allow for non-common confounders, with respect to which the instruments are exogenous. Compared to IV [Newey and Powell, 2003], we allow instruments to be exogenous conditional on some unobserved common confounders, for which a set of relevant observed variables exists. We prove point identification with outcome model and alternatively first stage restrictions. We provide a practical step-by-step guide to the ICC model assumptions and present the causal effect of education on income as a motivating example.
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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 | Nathan Kallus, Xiaojie Mao, and Masatoshi Uehara (2021) Causal inference under unmeasured confounding with negative controls: A minimax learning approach | 1.000 | 8 | 5 | 100% |
| 2 | Yifan Cui, Hongming Pu, Xu Shi, Wang Miao, and Eric Tchetgen Tchetgen (2020) Semiparametric proximal causal inference | 1.000 | 7 | 5 | 100% |
| 3 | Stéphane Bonhomme, Koen Jochmans, Jean-Marc Robin, et al (2016) Estimating multivariate latent-structure models | 0.928 | 4 | 3 | 100% |
| 4 | Whitney K Newey and James L Powell (2003) Instrumental variable estimation of nonparametric models | 0.928 | 4 | 3 | 100% |
| 5 | Guido W Imbens and Whitney K Newey (2009) Identification and estimation of triangular simultaneous equations models without additivity | 0.737 | 3 | 2 | 100% |
| 6 | Stephen V Cameron and Christopher Taber (2004) Estimation of educational borrowing constraints using returns to schooling | 0.511 | 2 | 1 | 100% |
| 7 | Pedro Carneiro and James J Heckman (2002) The evidence on credit constraints in post-secondary schooling | 0.511 | 2 | 1 | 100% |
| 8 | James J Heckman, Lance J Lochner, and Petra E Todd (2006) Earnings functions, rates of return and treatment effects: The mincer equation and beyond | 0.511 | 2 | 1 | 100% |
| 9 | Wang Miao, Zhi Geng, and Eric J Tchetgen Tchetgen (2018) Identifying causal effects with proxy variables of an unmeasured confounder | 0.511 | 2 | 1 | 100% |
| 10 | U.S. Department of Labor Bureau of Labor Statistics (2019) National longitudinal survey of youth 1997 cohort, 1997-2017 (rounds 1-18), 2019 | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 51 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Relaxing Instrument Exogeneity with Common Confounders | 0.644 | 2 | 2 |