4 citations · 4 across the 13 of their papers we have counts for
15 papers
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…
Towards Optimal Estimators for Randomized Control Trials
Harsh Parikh, Gabriel Levin-Konigsberg, Nilesh Tripuraneni +5
Randomized controlled trials (RCTs) are fundamental tools for causal inference across technology companies, pharmaceutical research, and federal agencies. While the standard differ…
Exploration and Experimentation with a Misspecified Simulator
Harsh Parikh, Gabriel Levin-Konigsberg, Dominique Perrault-Joncas +3
Randomized experiments are the standard for measuring causal effects that guide decisions, but are costly. Simulators calibrated to historical data offer a cheaper alternative, but…
The Epidemiology of Artificial Intelligence
Harsh Parikh, Tyler McCormick, Emily Johnson +3
Artificial intelligence (AI) systems increasingly shape how people access health information, make medical decisions, and receive care -- yet epidemiology lacks frameworks for meas…
Data (in)equities in data science: Dissecting systemic and systematic biases in pulse oximetry
Lillian Rountree, Harsh Parikh, Bhramar Mukherjee
Data equity is an emerging framework for responsible data science. However, its core concepts, including fairness, representativeness, and information bias, remain largely abstract…
When Does Agroforestry Income Reduce Deforestation? Evidence from a Natural Experiment in Madagascar
Camille DeSisto, Ranaivo Rasolofoson, Michelle Foley +1
Tropical deforestation and rural poverty are deeply intertwined, yet isolating the causal effect of income on forest loss remains challenging. We use the 2015 global vanilla price…
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…
Why are there many equally good models? An Anatomy of the Rashomon Effect
Harsh Parikh
The Rashomon effect -- the existence of multiple, distinct models that achieve nearly equivalent predictive performance -- has emerged as a fundamental phenomenon in modern machine…
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…
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…