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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Vydané v:	Biometrics Ročník 78; číslo 2; s. 624 - 635
Hlavní autori:	Yang, Jiabei, Dahabreh, Issa J., Steingrimsson, Jon A.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Blackwell Publishing Ltd 01.06.2022
Predmet:	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
On-line prístup:	Získať plný text
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