Estimating Heterogeneous Reactions to Experimental Treatments

43 Pages Posted: 25 Jan 2019 Last revised: 14 Jan 2020

See all articles by Christoph Engel

Christoph Engel

Max Planck Society for the Advancement of the Sciences - Max Planck Institute for Research on Collective Goods; University of Bonn - Faculty of Law & Economics; Erasmus University Rotterdam (EUR), Erasmus School of Law, Rotterdam Institute of Law and Economics, Students; Universität Osnabrück - Faculty of Law

Date Written: January 11, 2020

Abstract

Frequently in experiments there is not only variance in the reaction of participants to treatment. The heterogeneity is patterned: discernible types of participants react differently. In principle, a finite mixture model is well suited to simultaneously estimate the probability that a given participant belongs to a certain type, and the reaction of this type to treatment. Yet often, finite mixture models need more data than the experiment provides. The approach requires ex ante knowledge about the number of types. Finite mixture models are hard to estimate for panel data, which is what experiments often generate. For repeated experiments, this paper offers a simple two-step alternative that is much less data hungry, that allows to find the number of types in the data, and that allows for the estimation of panel data models. It combines machine learning methods with classic frequentist statistics.

Keywords: heterogeneous treatment effect, finite mixture model, panel data, two-step approach, machine learning, CART

JEL Classification: C14, C23, C91

Suggested Citation

Engel, Christoph, Estimating Heterogeneous Reactions to Experimental Treatments (January 11, 2020). MPI Collective Goods Discussion Paper, No. 2019/1, Available at SSRN: https://ssrn.com/abstract=3322322 or http://dx.doi.org/10.2139/ssrn.3322322

Christoph Engel (Contact Author)

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Universität Osnabrück - Faculty of Law

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