Data-driven sensitivity analysis for matching estimators

Giovanni Cerulli

London Stata Conference 2018 from Stata Users Group

Abstract: Matching is a popular estimator of the Average Treatment Effects (ATEs) within counterfactual observational studies. In recent years, however, many scholars have questioned the validity of this approach for causal inference, as its reliability draws heavily upon the so-called selection-on-observables assumption. When unobservable confounders are possibly at work, they say, it becomes hard to trust matching results, and the analyst should consider alternative methods suitable for tackling unobservable selection. Unfortunately, these alternatives require extra information that may be costly to obtain, or even not accessible. For this reason, some scholars have proposed matching sensitivity tests for the possible presence of unobservable selection. The literature sets out two methods: the Rosenbaum (1987) and the Ichino, Mealli, and Nannicini (2008) tests. Both are implemented in Stata. In this work, I propose a third and different sensitivity test for unobservable selection in Matching estimation based on a ‘leave-covariates-out’ (LCO) approach. Rooted in the machine learning literature, this sensitivity test recalls a bootstrap over different subsets of covariates and simulates various estimation scenarios to be compared with the baseline matching estimated by the analyst. Finally, I will present sensimatch, the Stata routine I developed to run this method, and provide some instructional applications on real datasets.

Date: 2018-10-15
New Economics Papers: this item is included in nep-big
References: Add references at CitEc
Citations:

Downloads: (external link)
http://repec.org/usug2018/Cerulli_2018_London.pdf (application/pdf)

Related works:
Journal Article: Data-driven sensitivity analysis for matching estimators (2019)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:boc:usug18:02

Access Statistics for this paper

More papers in London Stata Conference 2018 from Stata Users Group Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F Baum ().