From the 1 of 12 linked papers with an AI index.
12 papers
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…
Data (in)equities in data science: Dissecting systemic and systematic biases in pulse oximetry
Lillian Rountree, Harsh Parikh, Bhramar Mukherjee
The paper shows how statisticians can turn the abstract ideas of data equity into concrete, testable methods, using racial bias in pulse oximeter measurements as a case study to tr…
Mind the Sim-to-Real Gap & Think Like a Scientist
Harsh Parikh, Gabriel Levin-Konigsberg, Dominique Perrault-Joncas +1
Suppose a planner has a pre-trained simulator of a sequential decision problem and the option to run real experiments in the field. The simulator is cheap to query but inherits con…
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…
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…
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…