tesensitivity: A Stata Package for Assessing the Unconfoundedness Assumption
Matthew Masten () and
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Matthew Masten: Duke University
2019 Stata Conference from Stata Users Group
This talk will discuss a new set of methods for quantifying the robustness of treatment effects estimated under the unconfoundedness assumption (also known as selection on observables or conditional ignorability). Specifically, we estimate bounds on the ATE, the ATT, and the QTE under nonparametric relaxations of unconfoundedness indexed by a scalar sensitivity parameter c. These deviations allow for limited selection on unobservables, depending on the value of c. For large enough c, these bounds equal the no assumptions bounds. Our methods allow for both continuous and discrete outcomes, but require discrete treatments. We implement these methods in a new Stata package, tesensitivity, for easy use in practice. We illustrate how to use this package and these methods with an empirical application to the National Supported Work Demonstration program.
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Persistent link: https://EconPapers.repec.org/RePEc:boc:scon19:51
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