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Found 5 papers, 3 papers with code
Data-Driven Estimation of Heterogeneous Treatment Effects
In this work, we provide a survey of state-of-the-art data-driven methods for heterogeneous treatment effect estimation using machine learning, broadly categorizing them as methods that focus on counterfactual prediction and methods that directly estimate the causal effect.
Improving Data-driven Heterogeneous Treatment Effect Estimation Under Structure Uncertainty
To address this problem, we develop a feature selection method that considers each feature's value for HTE estimation and learns the relevant parts of the causal structure from data.
Heterogeneous Peer Effects in the Linear Threshold Model
The Linear Threshold Model is a widely used model that describes how information diffuses through a social network.
Heterogeneous Effects of Software Patches in a Multiplayer Online Battle Arena Game
The popularity of online gaming has grown dramatically, driven in part by streaming and the billion-dollar e-sports industry.
Learning Triggers for Heterogeneous Treatment Effects
The causal effect of a treatment can vary from person to person based on their individual characteristics and predispositions.
