Control Variables, Discrete Instruments, and Identification of Structural Functions

arXiv 15 Sep 2018 · Econometrics · publishedJournal of Econometrics (2020) · 24 citations (OpenAlex)

arXiv:1809.05706 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Control variables provide an important means of controlling for endogeneity in econometric models with nonseparable and/or multidimensional heterogeneity. We allow for discrete instruments, giving identification results under a variety of restrictions on the way the endogenous variable and the control variables affect the outcome. We consider many structural objects of interest, such as average or quantile treatment effects. We illustrate our results with an empirical application to Engel curve estimation.

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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

	Citing paper	Intensity	Mentions	Sections
1	Identification of Treatment Effects under Limited Exogenous Variation	0.950	7	4
2	Estimating a Continuous Treatment Model with Spillovers: A Control Function Approach	0.644	2	2
3	Estimating Causal Effects of Discrete and Continuous Treatments with Binary Instruments	0.644	2	2
4	Treatment Effect Estimation with Noisy Conditioning Variables	0.511	2	1
5	Heterogeneous Coefficients, Control Variables, and Identification of Multiple Treatment Effects	0.405	1	1
6	Long-term Causal Inference Under Persistent Confounding via Data Combination	0.405	1	1
7	Set-Valued Control Functions	0.405	1	1
8	Distributional Instruments: Identification and Estimation with Quantile Least Squares	0.405	1	1
9	Simultaneous Mean-Variance Regression	0.000	1	1
10	1820 Don't (fully) exclude me, it's not necessary! Causal inference with semi-IVs	0.000	1	1