4 citations · 4 across the 13 of their papers we have counts for
4 papers · 1 filter
Demystifying Proximal Causal Inference
Grace V. Ringlein, Trang Quynh Nguyen, Peter P. Zandi +2
Proximal causal inference (PCI) has emerged as a promising framework for identifying and estimating causal effects in the presence of unobserved confounders. While many traditional…
Precision Mental Health: Predicting Heterogeneous Treatment Effects for Depression through Data Integration
Carly L. Brantner, Trang Quynh Nguyen, Harsh Parikh +3
When treating depression, clinicians are interested in determining the optimal treatment for a given patient, which is challenging given the amount of treatments available. To adva…
Regularizing Extrapolation in Causal Inference
David Arbour, Harsh Parikh, Bijan Niknam +3
Many common estimators in machine learning and causal inference are linear smoothers, where the prediction is a weighted average of the training outcomes. Some estimators, such as…
A Cautionary Tale on Integrating Studies with Disparate Outcome Measures for Causal Inference
Harsh Parikh, Trang Quynh Nguyen, Elizabeth A. Stuart +2
Data integration approaches are increasingly used to enhance the efficiency and generalizability of studies. However, a key limitation of these methods is the assumption that outco…