activity
20182026
most citedIndependence weights for causal inference with continuous treatments

32 citations · 84 across the 22 of their papers we have counts for

collaborators

24 papers

stat.ME2026

Sharp Bounds for Treatment Effect Generalization under Outcome Distribution Shift

Amir Asiaee, Samhita Pal, Cole Beck +1

Generalizing treatment effects from a randomized trial to a target population requires the assumption that potential outcome distributions are invariant across populations after co…

stat.ME2026

Estimating causal effects of functional treatments with modified functional treatment policies

Ziren Jiang, Erjia Cui, Jared D. Huling

Functional data are increasingly prevalent in biomedical research. While functional data analysis has been established for decades, causal inference with functional treatments rema…

stat.ME2025

Data adaptive covariate balancing for causal effect estimation for high dimensional data

Simion De, Jared D. Huling

A key challenge in estimating causal effects from observational data is handling confounding and is commonly achieved through weighting methods that balance distribution of covaria…

stat.ME2025

Exploring the effects of mechanical ventilator settings with modified vector-valued treatment policies

Ziren Jiang, Philip S. Crooke, John J. Marini +1

Mechanical ventilation is critical for managing respiratory failure, but inappropriate ventilator settings can lead to ventilator-induced lung injury (VILI), increasing patient mor…

stat.ME2025

Heterogeneity-Aware Regression with Nonparametric Estimation and Structured Selection for Hospital Readmission Prediction

Wei Wang, Angela Bailey, Christopher Tignanelli +1

Readmission prediction is a critical but challenging clinical task, as the inherent relationship between high-dimensional covariates and readmission is complex and heterogeneous. D…

stat.ME2024

A Unified Framework for Causal Estimand Selection

Martha Barnard, Jared D. Huling, Julian Wolfson

Estimating the causal effect of a treatment or health policy with observational data can be challenging due to an imbalance of and a lack of overlap between treated and control cov…