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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
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
Exploring Transformer Backbones for Heterogeneous Treatment Effect EstimationCode1
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
Heterogeneous Treatment Effect Estimation using machine learning for Healthcare application: tutorial and benchmark—0
The Role of "Live" in Livestreaming Markets: Evidence Using Orthogonal Random Forest—0
On Inductive Biases for Heterogeneous Treatment Effect EstimationCode1
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