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Causal Inference from Hypothetical Evaluations

Author

Listed:
  • B. Douglas Bernheim
  • Daniel Björkegren
  • Jeffrey Naecker
  • Michael Pollmann
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

  • B. Douglas Bernheim & Daniel Björkegren & Jeffrey Naecker & Michael Pollmann, 2021. "Causal Inference from Hypothetical Evaluations," NBER Working Papers 29616, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:29616
    Note: PE
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    Cited by:

    1. Romuald Meango, 2023. "Using Probabilistic Stated Preference Analyses to Understand Actual Choices," Papers 2307.13966, arXiv.org.
    2. Ingvild Almås & Orazio Attanasio & Pamela Jervis, 2024. "Presidential Address: Economics and Measurement: New Measures to Model Decision Making," Econometrica, Econometric Society, vol. 92(4), pages 947-978, July.

    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

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