Using Probabilistic Stated Preference Analyses to Understand Actual Choices

TL;DR

This paper introduces a probabilistic approach to using stated preferences for better understanding and correcting for unobserved heterogeneity in actual choice data, enabling more accurate causal inference.

Contribution

It proposes a novel method that leverages probabilistic stated choices to identify unobserved heterogeneity and correct endogeneity in demand estimation.

Findings

The method effectively identifies unobserved heterogeneity.

It allows for causal effect estimation using actual choices.

The approach is compatible with standard Group Fixed Effects estimators.

Abstract

Can stated preferences help in counterfactual analyses of actual choice? This research proposes a novel approach to researchers who have access to both stated choices in hypothetical scenarios and actual choices. The key idea is to use probabilistic stated choices to identify the distribution of individual unobserved heterogeneity, even in the presence of measurement error. If this unobserved heterogeneity is the source of endogeneity, the researcher can correct for its influence in a demand function estimation using actual choices, and recover causal effects. Estimation is possible with an off-the-shelf Group Fixed Effects estimator.