9 citations · 10 across the 16 of their papers we have counts for
4 papers · 1 filter
Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data
Srikar Katta, Harsh Parikh, Cynthia Rudin +1
Many modern causal questions ask how treatments affect complex outcomes that are measured using wearable devices and sensors. Current analysis approaches require summarizing these…
Experimental Designs for Heteroskedastic Variance
Justin Weltz, Tanner Fiez, Alexander Volfovsky +4
Most linear experimental design problems assume homogeneous variance although heteroskedastic noise is present in many realistic settings. Let a learner have access to a finite set…
Safe and Interpretable Estimation of Optimal Treatment Regimes
Harsh Parikh, Quinn Lanners, Zade Akras +4
Recent statistical and reinforcement learning methods have significantly advanced patient care strategies. However, these approaches face substantial challenges in high-stakes cont…
A Double Machine Learning Approach to Combining Experimental and Observational Data
Harsh Parikh, Marco Morucci, Vittorio Orlandi +3
Experimental and observational studies often lack validity due to untestable assumptions. We propose a double machine learning approach to combine experimental and observational st…