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

Research output: Contribution to journal › Article › peer-review

Original language	English
Pages (from-to)	192-227
Journal	Health Services and Outcomes Research Methodology
Volume	22
Issue number	2
Early online date	9 Nov 2021
DOIs	https://doi.org/10.1007/s10742-021-00259-3
Publication status	Published - Jun 2022

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

author = "Noemi Kreif and Karla DiazOrdaz and {Moreno Serra}, Rodrigo and Andrew Mirelman and Taufik Hidayat and Marc Suhrcke",

note = "{\textcopyright} The Author(s) 2021",

year = "2022",

month = jun,

doi = "10.1007/s10742-021-00259-3",

language = "English",

volume = "22",

pages = "192--227",

journal = "Health Services and Outcomes Research Methodology",

issn = "1387-3741",

publisher = "Springer",

number = "2",

}

TY - JOUR

T1 - Estimating heterogeneous policy impacts using causal machine learning

T2 - a case study of health insurance reform in Indonesia

AU - Kreif, Noemi

AU - DiazOrdaz, Karla

AU - Moreno Serra, Rodrigo

AU - Mirelman, Andrew

AU - Hidayat, Taufik

AU - Suhrcke, Marc

PY - 2022/6

Y1 - 2022/6

U2 - 10.1007/s10742-021-00259-3

DO - 10.1007/s10742-021-00259-3

M3 - Article

SN - 1387-3741

VL - 22

SP - 192

EP - 227

JO - Health Services and Outcomes Research Methodology

JF - Health Services and Outcomes Research Methodology

IS - 2

ER -