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Title: Data-driven Sensitivity Analysis for Matching Estimators
Resulting in 1 citation.
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Cerulli, Giovanni |
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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. |
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Bibliography Citation
Cerulli, Giovanni. "Data-driven Sensitivity Analysis for Matching Estimators." Economics Letters 185 (December 2019): 108749.
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