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Treatment effects beyond the mean using distributional regression: Methods and guidance

Author

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  • Maike Hohberg
  • Peter Pütz
  • Thomas Kneib
Abstract
This paper introduces distributional regression also known as generalized additive models for location, scale and shape (GAMLSS) as a modeling framework for analyzing treatment effects beyond the mean. In contrast to mean regression models, GAMLSS relate each distributional parameter to covariates. Therefore, they can be used to model the treatment effect not only on the mean but on the whole conditional distribution. Since they encompass a wide range of different distributions, GAMLSS provide a flexible framework for modeling non-normal outcomes in which additionally nonlinear and spatial effects can easily be incorporated. We elaborate on the combination of GAMLSS with program evaluation methods including randomized controlled trials, panel data techniques, difference in differences, instrumental variables, and regression discontinuity design. We provide practical guidance on the usage of GAMLSS by reanalyzing data from the Mexican Progresa program. Contrary to expectations, no significant effects of a cash transfer on the conditional consumption inequality level between treatment and control group are found.

Suggested Citation

  • Maike Hohberg & Peter Pütz & Thomas Kneib, 2020. "Treatment effects beyond the mean using distributional regression: Methods and guidance," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-29, February.
    • Handle: RePEc:plo:pone00:0226514
    DOI: 10.1371/journal.pone.0226514
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    References listed on IDEAS

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    Cited by:

    1. Nathan Kallus & Miruna Oprescu, 2022. "Robust and Agnostic Learning of Conditional Distributional Treatment Effects," Papers 2205.11486, arXiv.org, revised Feb 2023.
    2. Kneib, Thomas & Silbersdorff, Alexander & Säfken, Benjamin, 2023. "Rage Against the Mean – A Review of Distributional Regression Approaches," Econometrics and Statistics, Elsevier, vol. 26(C), pages 99-123.
    3. Henning Schaak & Jens Rommel & Julian Sagebiel & Jesus Barreiro-Hurlé & Douadia Bougherara & Luigi Cemablo & Marija Cerjak & Tajana Čop & Mikołaj Czajkowski & María Espinosa-Goded & Julia Höhler & Car, 2022. "How Well Can Experts Predict Farmers' Risk Preferences ?," Post-Print hal-03738351, HAL.

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