Data-driven sensitivity analysis for matching estimators

Giovanni Cerulli

Economics Letters, 2019, vol. 185, issue C

Abstract: 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.

Keywords: Sensitivity analysis; Average treatment effects; Matching; Causal inference; Machine learning (search for similar items in EconPapers)
JEL-codes: C01 C14 C52 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (3)

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Working Paper: Data-driven sensitivity analysis for matching estimators (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:185:y:2019:i:c:s0165176519303763

DOI: 10.1016/j.econlet.2019.108749

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