``Estimating Heterogeneous Causal Effects of High-Dimensional Treatments: Application to Conjoint Analysis.''

 

  Abstract

Estimation of heterogeneous treatment effects is an active area of research in causal inference. Most of the existing methods, however, focus on estimating the conditional average treatment effects of a single, binary treatment given a set of pre-treatment covariates. In this paper, we propose a method to estimate the heterogeneous causal effects of high-dimensional treatments, which poses unique challenges in terms of estimation and interpretation. The proposed approach is based on a Bayesian mixture of regularized regressions to identify groups of units who exhibit similar patterns of treatment effects. By directly modeling cluster membership with covariates, the proposed methodology allows one to explore the unit characteristics that are associated with different patterns of treatment effects. Our motivating application is conjoint analysis, which is a popular survey experiment in social science and marketing research and is based on a high-dimensional factorial design. We apply the proposed methodology to the conjoint data, where survey respondents are asked to select one of two immigrant profiles with randomly selected attributes. We find that a group of respondents with a relatively high degree of prejudice appears to discriminate against immigrants from non-European countries like Iraq. An open-source software package is available for implementing the proposed methodology.

  Related Papers

de la Cuesta, Brandon, Naoki Egami, and Kosuke Imai. (2022). ``Improving the External Validity of Conjoint Analysis: The Essential Role of Profile Distribution.'' Political Analysis, Vol. 30, No. 1 (January), pp. 19-45.
Egami, Naoki, and Kosuke Imai. (2019). ``Causal Interaction in Factorial Experiments: Application to Conjoint Analysis.'' Journal of the American Statistical Association, Vol. 114, No. 526 (June), pp. 529-540.

© Kosuke Imai
 Last modified: Fri Jan 14 06:19:35 EST 2022