Recursive partitioning for heterogeneous causal effects
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Publication:2962333
DOI10.1073/pnas.1510489113zbMath1357.62190arXiv1504.01132OpenAlexW2305754340WikidataQ27320968 ScholiaQ27320968MaRDI QIDQ2962333
Publication date: 16 February 2017
Published in: Proceedings of the National Academy of Sciences (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1504.01132
cross-validationcausal inferencepotential outcomesheterogeneous treatment effectssupervised machine learning
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Uses Software
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- Causal Inference for Statistics, Social, and Biomedical Sciences
- Large Sample Properties of Matching Estimators for Average Treatment Effects
- Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score
- Random forests
- Observational studies.
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