Dynamic Treatment Effect

Dynamic treatment effects (DTE) research focuses on estimating the causal impact of interventions that change over time, addressing the challenge of time-varying confounding and high dimensionality. Current research emphasizes robust estimation methods, often employing deep neural networks or doubly robust approaches within a multi-stage learning framework to mitigate model misspecification and improve accuracy. These advancements are crucial for evaluating the effectiveness of dynamic interventions across diverse fields, such as personalized medicine and adaptive policy design, leading to more informed decision-making and improved outcomes.

Papers

November 1, 2024

Higher-Order Causal Message Passing for Experimentation with Complex Interference
Mohsen Bayati, Yuwei Luo, William Overman, Sadegh Shirani, Ruoxuan Xiong
Treatment Effect Causal Direction Catastrophic Interference Complexity Level Biased Causal Effect Dynamic Treatment Effect

July 11, 2024

Causal inference through multi-stage learning and doubly robust deep neural networks
Yuqian Zhang, Jelena Bradic
Deep Neural Network Causal Inference Multi Stage Robust Deep Conditional Average Treatment Effect Causal Inference Task Dynamic Treatment Effect

May 2, 2024

Dynamic Local Average Treatment Effects
Ravi B. Sojitra, Vasilis Syrgkanis
Treatment Effect Unobserved Confounders Dynamic Treatment Regime Time Varying Treatment Dynamic Treatment Effect

February 17, 2023

Post Reinforcement Learning Inference
Vasilis Syrgkanis, Ruohan Zhan
Reinforcement Learning Stationary Policy Counterfactual Path Dynamic Treatment Effect Post Prediction Inference

March 25, 2022

Automatic Debiased Machine Learning for Dynamic Treatment Effects and General Nested Functionals
Victor Chernozhukov, Whitney Newey, Rahul Singh, Vasilis Syrgkanis
Robust Estimation Propensity Score Model Debiasing Riesz Representer Dynamic Treatment Effect Dynamic Discrete Choice

November 12, 2021

Dynamic treatment effects: high-dimensional inference under model misspecification
Yuqian Zhang, Weijie Ji, Jelena Bradic
High Dimensional Robust Estimation Model Misspecification Double Robustness Dynamic Treatment Effect

Dynamic Treatment Effect

Papers

Higher-Order Causal Message Passing for Experimentation with Complex Interference

Causal inference through multi-stage learning and doubly robust deep neural networks

Dynamic Local Average Treatment Effects

Post Reinforcement Learning Inference

Automatic Debiased Machine Learning for Dynamic Treatment Effects and General Nested Functionals

Dynamic treatment effects: high-dimensional inference under model misspecification