5 papers
Dynamic covariate balancing: estimating treatment effects over time with potential local projections
Davide Viviano, Jelena Bradic
This paper studies the estimation and inference of treatment effects in panel data settings when treatments change dynamically over time. We propose a balancing method that allows…
Minimax Semiparametric Learning With Approximate Sparsity
Jelena Bradic, Victor Chernozhukov, Whitney K. Newey +1
Estimating linear, mean-square continuous functionals is a pivotal challenge in statistics. In high-dimensional contexts, this estimation is often performed under the assumption of…
The Decaying Missing-at-Random Framework: Model Doubly Robust Causal Inference with Partially Labeled Data
Yuqian Zhang, Abhishek Chakrabortty, Jelena Bradic
In modern large-scale observational studies, data collection constraints often result in partially labeled datasets, posing challenges for reliable causal inference, especially due…
Dynamic treatment effects: high-dimensional inference under model misspecification
Yuqian Zhang, Weijie Ji, Jelena Bradic
Estimating dynamic treatment effects is crucial across various disciplines, providing insights into the time-dependent causal impact of interventions. However, this estimation pose…
Estimating Treatment Effect under Additive Hazards Models with High-dimensional Covariates
Jue Hou, Jelena Bradic, Ronghui Xu
Estimating causal effects for survival outcomes in the high-dimensional setting is an extremely important topic for many biomedical applications as well as areas of social sciences…