Causal Inference from Hypothetical Evaluations

72 Pages Posted: 3 Jan 2022 Last revised: 4 Jun 2023

See all articles by B. Douglas Bernheim

B. Douglas Bernheim

Stanford University - Department of Economics; National Bureau of Economic Research (NBER)

Daniel Björkegren

Brown University - Department of Economics

Jeffrey Naecker

affiliation not provided to SSRN; Google

Michael Pollmann

Stanford University

Multiple version iconThere are 2 versions of this paper

Date Written: December 2021

Abstract

This paper explores methods for inferring the causal effects of treatments on choices by combining data on real choices with hypothetical evaluations. We propose a class of estimators, identify conditions under which they yield consistent estimates, and derive their asymptotic distributions. The approach is applicable in settings where standard methods cannot be used (e.g., due to the absence of helpful instruments, or because the treatment has not been implemented). It can recover heterogeneous treatment effects more comprehensively, and can improve precision. We provide proof of concept using data generated in a laboratory experiment and through a field application.

Suggested Citation

Bernheim, B. Douglas and Björkegren, Daniel and Naecker, Jeffrey and Naecker, Jeffrey and Pollmann, Michael, Causal Inference from Hypothetical Evaluations (December 2021). NBER Working Paper No. w29616, Available at SSRN: https://ssrn.com/abstract=3998789

B. Douglas Bernheim (Contact Author)

Stanford University - Department of Economics ( email )

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Daniel Björkegren

Brown University - Department of Economics ( email )

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HOME PAGE: http://dan.bjorkegren.com

Jeffrey Naecker

affiliation not provided to SSRN

Google ( email )

Michael Pollmann

Stanford University

Stanford, CA 94305
United States

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