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Heterogeneous Treatment Effect Estimation

Heterogeneous treatment effect (HTE) estimation is the task of quantifying how treatment effects vary across different individuals or subgroups.

Papers

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Showing 21–30 of 38 papers

TitleStatusHype
In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect EstimationCode0
Data-Driven Estimation of Heterogeneous Treatment Effects—0
Heterogeneous Treatment Effect Estimation for Observational Data using Model-based Forests—0
Improving Data-driven Heterogeneous Treatment Effect Estimation Under Structure UncertaintyCode0
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability—0
Efficient Heterogeneous Treatment Effect Estimation With Multiple Experiments and Multiple Outcomes—0
Flexible and Efficient Contextual Bandits with Heterogeneous Treatment Effect Oracles—0
GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation in Online Marketplace—0
Heterogeneous Effects of Software Patches in a Multiplayer Online Battle Arena Game—0
GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation Using Nonparametric Methods—0
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