Data-driven exclusion criteria for instrumental variable studiesDownload PDF

Published: 09 Feb 2022, Last Modified: 05 May 2023CLeaR 2022 PosterReaders: Everyone
Keywords: instrumental variables, exclusion criteria, compliance estimation, fuzzy regression discontinuity design, clinical guidelines, medical claims data, diabetes diagnostic criteria
TL;DR: We use compliance estimation to define exclusion criteria that improves the feasibility of instrumental variable studies.
Abstract: When using instrumental variables for causal inference, it is common practice to apply specific exclusion criteria to the data prior to estimation. This exclusion, critical for study design, is often done in an ad hoc manner, informed by a priori hypotheses and domain knowledge. In this study, we frame exclusion as a data-driven estimation problem, and apply flexible machine learning methods to estimate the probability of a unit complying with the instrument. We demonstrate how excluding likely noncompliers can increase power while maintaining valid treatment effect estimates. We show the utility of our approach with a fuzzy regression discontinuity analysis of the effect of initial diabetes diagnosis on follow-up blood sugar levels. Data-driven exclusion criterion can help improve both power and external validity for various quasi-experimental settings.
8 Replies

Loading