Heterogeneous Treatment Effect Estimation

Heterogeneous treatment effect (HTE) estimation aims to identify how the impact of a treatment varies across individuals or subgroups, moving beyond average treatment effects. Current research focuses on improving the robustness and accuracy of HTE estimation, particularly addressing challenges like selection bias, distribution shifts across populations (in-distribution vs. out-of-distribution), and the need for uncertainty quantification. This involves exploring various machine learning models, including neural networks (e.g., multilayer perceptrons, transformers), Bayesian methods (e.g., Bayesian causal forests, Gaussian process-based models), and tree-based approaches (e.g., causal forests), often incorporating techniques for subgroup identification and fairness considerations. The ability to accurately estimate HTEs has significant implications for personalized medicine, policy-making, and other fields requiring targeted interventions.