Estimating heterogeneous policy impacts using causal machine learning:a case study of health insurance reform in Indonesia

Kreif, Noemi, DiazOrdaz, Karla, Moreno Serra, Rodrigo orcid.org/0000-0002-6619-4560 et al. (3 more authors) (2022) Estimating heterogeneous policy impacts using causal machine learning:a case study of health insurance reform in Indonesia. Health Services and Outcomes Research Methodology. pp. 192-227. ISSN 1572-9400

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Item Type: Article
Authors/Creators:
Copyright, Publisher and Additional Information:

© The Author(s) 2021
Dates:
  • Accepted: 28 September 2021
  • Published (online): 9 November 2021
  • Published: June 2022
Institution: The University of York
Academic Units: The University of York > Faculty of Social Sciences (York) > Centre for Health Economics (York)
Depositing User: Pure (York)
Date Deposited: 25 Feb 2022 11:40
Last Modified: 09 Apr 2025 23:33
Published Version: https://doi.org/10.1007/s10742-021-00259-3
Status: Published
Refereed: Yes
Identification Number: 10.1007/s10742-021-00259-3
Open Archives Initiative ID (OAI ID):

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Description: Estimating heterogeneous policy impacts using causal machine learning: a case study of health insurance reform in Indonesia

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