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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Vydáno v:Biometrics Ročník 78; číslo 2; s. 624 - 635
Hlavní autoři: Yang, Jiabei, Dahabreh, Issa J., Steingrimsson, Jon A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Blackwell Publishing Ltd 01.06.2022
Témata:
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
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Abstract 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 treatment effect estimators appropriate for observational data. We describe inverse probability weighting, g‐formula, and doubly robust estimators of subgroup‐specific treatment effects, derive their asymptotic properties, and use them to construct splitting criteria for the CIT algorithms. We study the performance of the algorithms in simulations and implement them to analyze data from an observational study that evaluated the effectiveness of right heart catheterization for critically ill patients.
AbstractList 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 treatment effect estimators appropriate for observational data. We describe inverse probability weighting, g‐formula, and doubly robust estimators of subgroup‐specific treatment effects, derive their asymptotic properties, and use them to construct splitting criteria for the CIT algorithms. We study the performance of the algorithms in simulations and implement them to analyze data from an observational study that evaluated the effectiveness of right heart catheterization for critically ill patients.
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 treatment effect estimators appropriate for observational data. We describe inverse probability weighting, g‐formula, and doubly robust estimators of subgroup‐specific treatment effects, derive their asymptotic properties, and use them to construct splitting criteria for the CIT algorithms. We study the performance of the algorithms in simulations and implement them to analyze data from an observational study that evaluated the effectiveness of right heart catheterization for critically ill patients.
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 treatment effect estimators appropriate for observational data. We describe inverse probability weighting, g-formula, and doubly robust estimators of subgroup-specific treatment effects, derive their asymptotic properties, and use them to construct splitting criteria for the CIT algorithms. We study the performance of the algorithms in simulations and implement them to analyze data from an observational study that evaluated the effectiveness of right heart catheterization for critically ill patients.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 treatment effect estimators appropriate for observational data. We describe inverse probability weighting, g-formula, and doubly robust estimators of subgroup-specific treatment effects, derive their asymptotic properties, and use them to construct splitting criteria for the CIT algorithms. We study the performance of the algorithms in simulations and implement them to analyze data from an observational study that evaluated the effectiveness of right heart catheterization for critically ill patients.
Author Dahabreh, Issa J.
Yang, Jiabei
Steingrimsson, Jon A.
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Keywords causal inference
machine learning
heterogeneity of treatment effects
doubly robust estimators
recursive partitioning
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Snippet We introduce causal interaction tree (CIT) algorithms for finding subgroups of individuals with heterogeneous treatment effects in observational data. The CIT...
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SubjectTerms 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
Title Causal interaction trees: Finding subgroups with heterogeneous treatment effects in observational data
URI https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fbiom.13432
https://www.ncbi.nlm.nih.gov/pubmed/33527341
https://www.proquest.com/docview/2684614566
https://www.proquest.com/docview/2485516327
https://www.proquest.com/docview/2718292829
Volume 78
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