Willingness to Say? Optimal Survey Design for Prediction

Charlotte Cavaillé, Karine van Der Straeten () and Daniel L. Chen ()
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Charlotte Cavaillé: University of Michigan [Ann Arbor] - University of Michigan System
Karine van Der Straeten: TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement
Daniel L. Chen: TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement

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Abstract: Survey design often approximates a prediction problem: the goal is to select instruments that best predict the value of an unobserved construct or a future outcome. We demonstrate how advances in machine learning techniques can help choose among competing instruments. First, we randomly assign respondents to one of four survey instruments to predict a behavior defined by our validation strategy. Next, we assess the optimal instrument in two stages. A machine learning model first predicts the behavior using individual covariates and survey responses. Then, using doubly robust welfare maximization and prediction error from the first stage, we learn the optimal survey method and examine how it varies across education levels.

Date: 2023-04-07
New Economics Papers: this item is included in nep-big
Note: View the original document on HAL open archive server: https://hal.science/hal-04062637v1
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