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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 11–20 of 38 papers

TitleStatusHype
GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation Using Nonparametric Methods—0
GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation in Online Marketplace—0
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability—0
Heterogeneous treatment effect estimation with high-dimensional data in public policy evaluation -- an application to the conditioning of cash transfers in Morocco using causal machine learning—0
Class flipping for uplift modeling and Heterogeneous Treatment Effect estimation on imbalanced RCT data—0
Data-Driven Estimation of Heterogeneous Treatment Effects—0
Deep Learning for Causal Inference: A Comparison of Architectures for Heterogeneous Treatment Effect Estimation—0
Heterogeneous Effects of Software Patches in a Multiplayer Online Battle Arena Game—0
Heterogeneous Treatment Effect Estimation using machine learning for Healthcare application: tutorial and benchmark—0
Heterogeneous treatment effect estimation with subpopulation identification for personalized medicine in opioid use disorder—0
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