Arbeitspapier

Estimating heterogeneous reactions to experimental treatments

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.

Sprache
Englisch

Erschienen in
Series: Discussion Papers of the Max Planck Institute for Research on Collective Goods ; No. 2019/1

Klassifikation
Wirtschaft
Semiparametric and Nonparametric Methods: General
Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
Design of Experiments: Laboratory, Individual
Thema
heterogeneous treatment effect
finite mixture model
panel data
two-step approach
machine learning
CART

Ereignis
Geistige Schöpfung
(wer)
Engel, Christoph
Ereignis
Veröffentlichung
(wer)
Max Planck Institute for Research on Collective Goods
(wo)
Bonn
(wann)
2019

Handle
Letzte Aktualisierung
20.09.2024, 08:21 MESZ

Datenpartner

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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Engel, Christoph
  • Max Planck Institute for Research on Collective Goods

Entstanden

  • 2019

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