10 citations · 24 across the 10 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…
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…
Variable Importance Matching for Causal Inference
Quinn Lanners, Harsh Parikh, Alexander Volfovsky +2
Our goal is to produce methods for observational causal inference that are auditable, easy to troubleshoot, accurate for treatment effect estimation, and scalable to high-dimension…