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Activity Number: 271 - Methodological Challenges for Handling Unmeasured Confounders in Causal Inference with Social Science Data
Type: Invited
Date/Time: Tuesday, August 1, 2017 : 8:30 AM to 10:20 AM
Sponsor: Social Statistics Section
Abstract #321952 View Presentation
Title: Inference in an Instrumental Variables Model with Heterogeneous Treatment Effects and Many Instruments
Author(s): Michal Kolesar* and Kirill Evdokimov
Companies: Princeton University and Princeton University
Keywords: instrumental variables ; many instruments ; treatment effect heterogeneity ; local average treatment effects ; standard errors

We study inference in an instrumental variables model with heterogeneous treatment effects and possibly many instruments and/or exogenous covariates. In this case the two-step estimators such as two-stage least squares (TSLS) or versions of the jackknife instrumental variables (JIV) estimator estimate a particular weighted average of local average treatment effects. The weights in these estimands depend on the first-stage coefficients, and either the sample or population distribution of the the covariates and instruments, depending on whether they are treated as fixed or random. We give new asymptotic variance formulas for TSLS and JIV estimators, and derive consistent estimators of this variance. Compared to the case with homogeneous treatment effects, the variance expression contains an additional term reflecting variability of the local average treatment effects that is quantitatively important.

Authors who are presenting talks have a * after their name.

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