Double Robustness for Complier Parameters and a Semiparametric Test for Complier Characteristics

2019/09/10 by Rahul Singh, Liyang Sun, Singh, Rahul +1
Economics, Econometrics and Finance · Neuroscience · #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #FOS: Mathematics #Firm Innovation and Growth #Innovation Policy and R&D #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neuroscience and Music Perception #Statistics Theory (math.ST)

paper · pdf · doi:10.48550/arxiv.1909.05244

openalex publication_date 2019/09/10 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28

Abstract

We propose a semiparametric test to evaluate (i) whether different instruments induce subpopulations of compliers with the same observable characteristics on average, and (ii) whether compliers have observable characteristics that are the same as the full population on average. The test is a flexible robustness check for the external validity of instruments. We use it to reinterpret the difference in LATE estimates that Angrist and Evans (1998) obtain when using different instrumental variables. To justify the test, we characterize the doubly robust moment for Abadie (2003)'s class of complier parameters, and we analyze a machine learning update to κ weighting.

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