TY  - RPRT
CY  - Toulouse
ID  - publications47539
UR  - http://tse-fr.eu/pub/128022
A1  - Cavaillé, Charlotte
A1  - Van Der Straeten, Karine
A1  - Chen, Daniel L.
Y1  - 2023/03//
N2  - 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.
PB  - TSE Working Paper
T3  - TSE Working Paper
M1  - working_paper
TI  - Willingness to say? Optimal survey design for prediction
AV  - public
EP  - 23
ER  -