Search Results

Title: Data-driven Sensitivity Analysis for Matching Estimators
Resulting in 1 citation.
1. Cerulli, Giovanni
Data-driven Sensitivity Analysis for Matching Estimators
Economics Letters 185 (December 2019): 108749.
Also: https://www.sciencedirect.com/science/article/pii/S0165176519303763
Cohort(s): Young Women
Publisher: Elsevier
Keyword(s): Modeling; Statistical Analysis; Unions; Wages

This paper proposes a sensitivity analysis test of unobservable selection for matching estimators based on a "leave-one-covariate-out" (LOCO) algorithm. Rooted in the machine learning literature, this sensitivity test performs a bootstrap over different subsets of covariates, and simulates various estimation scenarios to be compared with the baseline matching results. We provide an empirical application, comparing results with more traditional sensitivity tests.
Bibliography Citation
Cerulli, Giovanni. "Data-driven Sensitivity Analysis for Matching Estimators." Economics Letters 185 (December 2019): 108749.