Choosing exogeneity assumptions in potential outcome models

Matthew Masten and Alexandre Poirier

The Econometrics Journal, 2023, vol. 26, issue 3, 327-349

Abstract: SummaryThere are many kinds of exogeneity assumptions. How should researchers choose among them? When exogeneity is imposed on an unobservable like a potential outcome, we argue that the form of exogeneity should be chosen based on the kind of selection on unobservables it allows. Consequently, researchers can assess the plausibility of any exogeneity assumption by studying the distributions of treatment given the unobservables that are consistent with that assumption. We use this approach to study two common exogeneity assumptions: quantile and mean independence. We show that both assumptions require a kind of nonmonotonic relationship between treatment and the potential outcomes. We discuss how to assess the plausibility of this kind of treatment selection. We also show how to define a new and weaker version of quantile independence that allows for monotonic selection on unobservables. We then show the implications of the choice of exogeneity assumption for identification. We apply these results in an empirical illustration of the effect of child soldiering on wages.

Keywords: Selection on unobservables; nonparametric identification; treatment effects; partial identification; sensitivity analysis (search for similar items in EconPapers)
Date: 2023
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Working Paper: Choosing Exogeneity Assumptions in Potential Outcome Models (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:oup:emjrnl:v:26:y:2023:i:3:p:327-349.

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