Causal interaction trees: Finding subgroups with heterogeneous treatment effects in observational data

We introduce causal interaction tree (CIT) algorithms for finding subgroups of individuals with heterogeneous treatment effects in observational data. The CIT algorithms are extensions of the classification and regression tree algorithm that use splitting criteria based on subgroup‐specific treatmen...

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Veröffentlicht in:	Biometrics Jg. 78; H. 2; S. 624 - 635
Hauptverfasser:	Yang, Jiabei, Dahabreh, Issa J., Steingrimsson, Jon A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Blackwell Publishing Ltd 01.06.2022
Schlagworte:	Algorithms Asymptotic properties catheters causal inference Criteria data analysis doubly robust estimators Estimators heart heterogeneity of treatment effects machine learning observational studies probability recursive partitioning Regression analysis Splitting Statistical analysis Subgroups trees causal inference machine learning heterogeneity of treatment effects doubly robust estimators recursive partitioning
ISSN:	0006-341X, 1541-0420, 1541-0420
Online-Zugang:	Volltext
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