Counterfactual Sensitivity and Robustness

Timothy Christensen, Benjamin Connault

    Research output: Contribution to journalArticlepeer-review


    We propose a framework for analyzing the sensitivity of counterfactuals to parametric assumptions about the distribution of latent variables in structural models. In particular, we derive bounds on counterfactuals as the distribution of latent variables spans nonparametric neighborhoods of a given parametric specification while other “structural” features of the model are maintained. Our approach recasts the infinite-dimensional problem of optimizing the counterfactual with respect to the distribution of latent variables (subject to model constraints) as a finite-dimensional convex program. We also develop an MPEC version of our method to further simplify computation in models with endogenous parameters (e.g., value functions) defined by equilibrium constraints. We propose plug-in estimators of the bounds and two methods for inference. We also show that our bounds converge to the sharp nonparametric bounds on counterfactuals as the neighborhood size becomes large. To illustrate the broad applicability of our procedure, we present empirical applications to matching models with transferable utility and dynamic discrete choice models.

    Original languageEnglish (US)
    Pages (from-to)263-298
    Number of pages36
    Issue number1
    StatePublished - Jan 2023


    • Robustness
    • ambiguity
    • global sensitivity analysis
    • misspecification
    • model uncertainty

    ASJC Scopus subject areas

    • Economics and Econometrics


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