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

/ August 19, 2022/

When:
September 13, 2022 @ 12:00 pm – 1:15 pm
2022-09-13T12:00:00-04:00
2022-09-13T13:15:00-04:00

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).

 

Join Zoom Meeting

https://wse.zoom.us/j/98624413365

 

Meeting ID: 986 2441 3365

One tap mobile

+13017158592,,98624413365# US (Washington DC)

+16469313860,,98624413365# US

 

Dial by your location

+1 301 715 8592 US (Washington DC)

+1 646 931 3860 US

+1 309 205 3325 US

+1 312 626 6799 US (Chicago)

+1 646 558 8656 US (New York)

+1 669 900 6833 US (San Jose)

+1 719 359 4580 US

+1 253 215 8782 US (Tacoma)

+1 346 248 7799 US (Houston)

+1 386 347 5053 US

+1 564 217 2000 US

+1 669 444 9171 US

Meeting ID: 986 2441 3365

Find your local number: https://wse.zoom.us/u/asoOElnUp

 

Join by SIP

98624413365@zoomcrc.com

 

Join by H.323

162.255.37.11 (US West)

162.255.36.11 (US East)

115.114.131.7 (India Mumbai)

115.114.115.7 (India Hyderabad)

213.19.144.110 (Amsterdam Netherlands)

213.244.140.110 (Germany)

103.122.166.55 (Australia Sydney)

103.122.167.55 (Australia Melbourne)

149.137.40.110 (Singapore)

64.211.144.160 (Brazil)

149.137.68.253 (Mexico)

69.174.57.160 (Canada Toronto)

65.39.152.160 (Canada Vancouver)

207.226.132.110 (Japan Tokyo)

149.137.24.110 (Japan Osaka)

Meeting ID: 986 2441 3365

 

 

Share this Post