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Enabling Decision-Making with the Modified Causal Forest: Policy Trees for Treatment Assignment

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  • Hugo Bodory
  • Federica Mascolo
  • Michael Lechner

Abstract

Decision-making plays a pivotal role in shaping outcomes in various disciplines, such as medicine, economics, and business. This paper provides guidance to practitioners on how to implement a decision tree designed to address treatment assignment policies using an interpretable and non-parametric algorithm. Our Policy Tree is motivated on the method proposed by Zhou, Athey, and Wager (2023), distinguishing itself for the policy score calculation, incorporating constraints, and handling categorical and continuous variables. We demonstrate the usage of the Policy Tree for multiple, discrete treatments on data sets from different fields. The Policy Tree is available in Python's open-source package mcf (Modified Causal Forest).

Suggested Citation

  • Hugo Bodory & Federica Mascolo & Michael Lechner, 2024. "Enabling Decision-Making with the Modified Causal Forest: Policy Trees for Treatment Assignment," Papers 2406.02241, arXiv.org.
    • Handle: RePEc:arx:papers:2406.02241
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    References listed on IDEAS

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    1. Katherine Baicker & Amy Finkelstein, 2018. "The Impact of Medicaid Expansion on Voter Participation: Evidence from the Oregon Health Insurance Experiment," NBER Working Papers 25244, National Bureau of Economic Research, Inc.
    2. Toru Kitagawa & Aleksey Tetenov, 2021. "Equality-Minded Treatment Choice," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(2), pages 561-574, March.
    3. Eric Mbakop & Max Tabord‐Meehan, 2021. "Model Selection for Treatment Choice: Penalized Welfare Maximization," Econometrica, Econometric Society, vol. 89(2), pages 825-848, March.
    4. Toru Kitagawa & Aleksey Tetenov, 2018. "Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice," Econometrica, Econometric Society, vol. 86(2), pages 591-616, March.
    5. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org.
    6. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    7. Charles F. Manski, 2004. "Statistical Treatment Rules for Heterogeneous Populations," Econometrica, Econometric Society, vol. 72(4), pages 1221-1246, July.
    8. Amy Finkelstein & Nathaniel Hendren & Erzo F. P. Luttmer, 2019. "The Value of Medicaid: Interpreting Results from the Oregon Health Insurance Experiment," Journal of Political Economy, University of Chicago Press, vol. 127(6), pages 2836-2874.
    9. Luke Keele & Dylan S. Small, 2021. "Comparing Covariate Prioritization via Matching to Machine Learning Methods for Causal Inference Using Five Empirical Applications," The American Statistician, Taylor & Francis Journals, vol. 75(4), pages 355-363, October.
    10. Dean S. Karlan & Jonathan Zinman, 2008. "Credit Elasticities in Less-Developed Economies: Implications for Microfinance," American Economic Review, American Economic Association, vol. 98(3), pages 1040-1068, June.
    11. Susan Athey & Stefan Wager, 2021. "Policy Learning With Observational Data," Econometrica, Econometric Society, vol. 89(1), pages 133-161, January.
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    Cited by:

    1. Federica Mascolo & Nora Bearth & Fabian Muny & Michael Lechner & Jana Mareckova, 2024. "The Heterogeneous Effects of Active Labour Market Policies in Switzerland," Papers 2410.23322, arXiv.org.

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