Working Paper: NBER ID: w29616
Authors: B. Douglas Bernheim; Daniel Bjrkegren; 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.
Keywords: Causal Inference; Hypothetical Evaluations; Treatment Effects
JEL Codes: C13; D12
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Combining hypothetical evaluations with observational data (C90) | Recover average treatment effects (C22) |
| Hypothetical evaluations (C12) | Overstate willingness-to-pay (D11) |
| Method (Y20) | Address biases in hypothetical evaluations (C90) |
| Method (Y20) | Recover heterogeneous treatment effects (C21) |
| Method (Y20) | Improve precision in estimating treatment effects (C22) |
| Treatment (C22) | Choices (Y90) |