4 citations · 4 across the 13 of their papers we have counts for
9 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…
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…