How to detect heterogeneity in conjoint experiments

Robinson, Thomas ORCID: 0000-0001-7097-1599 and M. Duch, Raymond (2024) How to detect heterogeneity in conjoint experiments. Journal of Politics, 86 (2). ISSN 0022-3816

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Abstract

Conjoint experiments are fast becoming one of the dominant experimental methods within the social sciences. Despite recent efforts to model heterogeneity within this type of experiment, the relationship between the conjoint design and lower-level causal estimands is underdeveloped. In this paper, we clarify how conjoint heterogeneity can be construed as a set of nested, causal parameters that correspond to the levels of the conjoint design. We then use this framework to propose a new estimation strategy, using machine learning, that better allows researchers to evaluate treatment effect heterogeneity. We also provide novel tools for classifying and analysing heterogeneity post-estimation using partitioning algorithms. Replicating two conjoint experiments, we demonstrate our theoretical argument, and show how this method helps estimate and detect substantive patterns of heterogeneity. To accompany this paper, we provide new a R package, cjbart, that allows researchers to model heterogeneity in their experimental conjoint data.

Item Type:	Article
Official URL:	https://www.journals.uchicago.edu/toc/jop/current
Additional Information:	© 2024 Southern Political Science Association
Divisions:	Methodology
Subjects:	J Political Science H Social Sciences > H Social Sciences (General)
Date Deposited:	06 Oct 2023 10:30
Last Modified:	19 Apr 2024 09:12
URI:	http://eprints.lse.ac.uk/id/eprint/120376

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