MINDS & CIS Fall Seminar Series: Harsh Parikh, “Interpretable Causal Inference for High-Stakes Decision Making”

/ August 19, 2022/

September 13, 2022 @ 12:00 pm – 1:15 pm

Tuesdays, 12pm-1:15pm

Held virtually in person at Clark 110 & over Zoom

“Interpretable Causal Inference for High-Stakes Decision Making”

Harsh Parikh

Ph.D. Candidate

Duke University

Abstract: Many fundamental problems affecting the care of critically ill patients lead to similar analytical challenges: physicians cannot easily estimate the effects of at-risk medical conditions or treatments (which is problematic for treatment decisions) because the causal effects of medical conditions and drugs are entangled. They also cannot easily perform studies: there are not enough critically ill patients for high-dimensional observational causal inference analysis, and randomized controlled trials often cannot ethically be conducted. Our work introduces a general framework that can help estimate heterogeneous causal effects from high-dimensional patient-level data under these conditions. Each step of our framework is designed to be interpretable. Importantly, we leverage established mechanistic models to describe personalized decision-response interactions, allowing us to identify individuals who might react similarly to treatments. We learn a flexible distance metric on the space of covariates to perform almost exact matching for estimating the medium and long term causal effects. The learned distance metric stretches the covariate space according to each covariate’s contribution to prognosis: this stretching means that mismatches on important covariates carry a larger penalty than mismatches on irrelevant covariates. The matched group we construct for each patient can be validated, or possibly, criticized. In the context of medical data, this validation can be performed via a chart review that provides a qualitative assessment of the matches in terms of information that was not directly used in the matching procedure.

Biography: Harsh Parikh is a Ph.D. Candidate at Duke University working with Dr. Cynthia Rudin, Dr. Alexander Volfvosky, and Dr. Sudeepa Roy as a part of Almost Matching Exactly Lab. His research interest includes working on causal inference methodology with applications in critical care, public health, or education. His current research work includes (i) interpretable-and-accurate matching methods for high-stakes scenarios, (ii) methods for causal inference on social network/relational data, and (iii) frameworks to combine experimental and observational data. He has ongoing active collaboration with neuro-physicians at MGH and researchers at Amazon Science. He also received the Amazon Graduate Research Fellowship (Sept 2020 – Jan 2022). He received B.Tech in Computer Science from IIT Delhi (2015) and M.S in Economics and Computation from Duke University (2018).


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