4 citations · 4 across the 13 of their papers we have counts for
7 papers · 1 filter
Proximal causal inference through cross-proxy balancing
Grace V. Ringlein, Trang Q. Nguyen, Elizabeth A. Stuart +1
Proximal causal inference identifies causal effects in the presence of unmeasured confounding by drawing on two sets of proxy variables. Identification typically utilizes bridge fu…
Controllable Generative Sandbox for Causal Inference
Qi Zhang, Harsh Parikh, Ashley Naimi +3
Method validation and study design in causal inference rely on synthetic data with known counterfactuals. Existing simulators trade off distributional realism, the ability to captu…
TEA-Time: Transporting Effects Across Time
Harsh Parikh, Gabriel Levin-Konigsberg, Dominique Perrault-Joncas +1
Treatment effects estimated from a randomized controlled trial are local not only to the study population but also to the time at which the trial was conducted. The literature on g…
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…
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…
Towards Generalizing Inferences from Trials to Target Populations
Melody Y Huang, Harsh Parikh
Randomized Controlled Trials (RCTs) are pivotal in generating internally valid estimates with minimal assumptions, serving as a cornerstone for researchers dedicated to advancing c…
Who Are We Missing? A Principled Approach to Characterizing the Underrepresented Population
Harsh Parikh, Rachael Ross, Elizabeth Stuart +1
Randomized controlled trials (RCTs) serve as the cornerstone for understanding causal effects, yet extending inferences to target populations presents challenges due to effect hete…