Causal Interaction Trees: Tree-Based Subgroup Identification for Observational Data
We propose Causal Interaction Trees for identifying subgroups of participants that have enhanced treatment effects using observational data. We extend the Classification and Regression Tree algorithm by using splitting criteria that focus on maximizing between-group treatment effect heterogeneity based on subgroup-specific treatment effect estimators to dictate decision-making in the algorithm. We derive properties of three subgroup-specific treatment effect estimators that account for the observational nature of the data -- inverse probability weighting, g-formula and doubly robust estimators. We study the performance of the proposed algorithms using simulations and implement the algorithms in an observational study that evaluates the effectiveness of right heart catheterization on critically ill patients.
Code (1)
Tasks
Decision MakingregressionSimilar Papers 제목 키워드 기반
LLM-based Agents for Automated Confounder Discovery and Subgroup Analysis in Causal Inference
Estimating individualized treatment effects from observational data presents a persistent challenge due to unmeasured confounding and structural bias. Causal Machine Learning (causal ML) methods, such as causal trees and…
Causal InferenceCausal Rule Ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects
In health and social sciences, it is critically important to identify subgroups of the study population where there is notable heterogeneity of treatment effects (HTE) with respect to the population average. Decision tre…
Causal InferenceEpidemiologyPSICA: decision trees for probabilistic subgroup identification with categorical treatments
Personalized medicine aims at identifying best treatments for a patient with given characteristics. It has been shown in the literature that these methods can lead to great improvements in medicine compared to traditiona…
NutritionEfficient Subgroup Analysis via Optimal Trees with Global Parameter Fusion
Identifying and making statistical inferences on differential treatment effects (commonly known as subgroup analysis in clinical research) is central to precision health. Subgroup analysis allows practitioners to pinpoin…
Learning Subgroups with Maximum Treatment Effects without Causal Heuristics
Discovering subgroups with the maximum average treatment effect is crucial for targeted decision making in domains such as precision medicine, public policy, and education. While most prior work is formulated in the pote…
Decision Making