Summary
I develop machine learning–aided causal inference approaches to solve high-stakes problems that are: (i) Accurate, enabling estimation of heterogeneous treatment effects in complex scenarios with limited data; (ii) Trustworthy, allowing domain experts to understand the machinery, validate underlying assumptions, and identify where predictions may be unreliable; and (iii) Domain-conscious, leveraging domain context and knowledge to come up with applicable solutions, reducing the research-to-practice gap.
Current Affiliations
2025–Present
Tenure-Track Assistant Professor
Department of Biostatistics, Yale School of Public Health
2025–Present
Assistant Professor (Secondary Appointment)
Department of Statistics and Data Science, Yale University
2025–Present
Affiliated Faculty
Foundations of Data Science, Yale University
2025–Present
Affiliated Faculty
Institution for Social and Policy Studies (ISPS), Yale University
2025–Present
Guest Researcher
Danish Centre for Health Economics, Syddansk Universitet
2025–Present
Affiliate Researcher
Johns Hopkins Bloomberg School of Public Health
2025–Present
Applied Scientist III
Amazon.com · Supply Chain Optimization Technologies
Academic Training
2023–2025
Postdoctoral Fellow
Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics
Trustworthy Causal Inference for Transportability and Generalizability
Advisors: Elizabeth Stuart, Kara Rudolph
2018–2023
Ph.D. in Computer Science
Duke University, Department of Computer Science
Causal Inference for High-Stakes Decisions
Advisors: Cynthia Rudin, Alexander Volfovsky, Sudeepa Roy
★ Outstanding PhD Dissertation Award 2023 · Certificate in College Teaching
2016–2018
M.S. Economics & Computation
Duke University, Department of Economics
Advisors: Vincent Conitzer, Charles Becker
2011–2015
B.Tech. Computer Science & Engineering
Indian Institute of Technology (IIT) Delhi
Advisor: Parag Singla
Publications
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Honors & Awards
2024Future Leader in Data Science and AI, Michigan Institute for Data & AI in Society
2024Selected for Building Future Faculty Program, North Carolina State University
2023Outstanding PhD Dissertation Award, Dept. of Computer Science, Duke University
2023Uncertainty in Artificial Intelligence Conference Travel Award
2022Finalist, Two Sigma PhD Fellowship
2022International Conference on Machine Learning Conference Travel Award
2022Certificate in College Teaching, Duke University
2020–22Amazon Graduate Research Fellowship
2020Invited Talk, IIT Gandhinagar "Sabarmati Young Researcher's Seminar Series"
2016–18Duke Economics Master's Scholar Award, Duke University
2016Runner's Up, Global Healthcare Summit (Non-invasive blood glucose sensor)
2013–14Charpak (Student Exchange) French Government Scholarship, University of Lorraine
2013Summer Undergraduate Research Award (UROP), IIT Delhi
2011–12IIT Delhi Semester Merit Award
2012Runner's Up, CanSat USA by AAS, AIAA, JPL, NASA, NRL
2009–11Manish Bhatt Scholarship, Excellence in Computer Science
Additional Professional Experience
May–Aug 2022
Research Intern
Meta (Facebook) · Core Data Science · New York
Inferring Network Interference in Randomized Controlled Trials
2020 & 2021
Applied Scientist Intern
Amazon.com · Seller Fees and Profitability · Seattle
Evaluating Causal Inference Methods
Jun–Jul 2017
Research Intern
The Urban Institute · International Development and Governance · Washington DC
Public Transport and Rental Markets; Women Empowerment and Labor Force Participation
Mar–Jul 2016
Research Fellow
Vision India Foundation · Evidence-based Public Policy Analysis · New Delhi
Impact Analysis of National Rural Employment Guarantee Act
Jul 2015–May 2016
Research Engineer
IBM India Research Laboratory · Data Fusion & Graph Analytics · New Delhi
Social Network Data Analysis for Law Enforcement
May–Jul 2014
Software Engineering Intern
Arista Networks · Emerging Technologies · Bangalore
Protocol for Audio-Video Bridging (AVB) Switches
Invited & Conference Talks
Causal Inference Beyond Positivity Assumption
IMSI Workshop: New Horizons on Model Transportability and Data Integration, Chicago (Jun 2026)
Transporting Effects Across Networks
Network Science 2026, CausNetS: Toward a Causal Network Science, Invited Speaker (2026)
Reinforcement Learning for Optimal Decisions in Public Health (Discussant)
ENAR 2026, Houston (Mar 2026)
Regularizing Extrapolation in Causal Inference
Yale Foundations of Data Science Colloquium, New Haven (Jan 2026) · Stats Seminar (Jan 2026) · IIT Gandhinagar (Jan 2026) · Indian Statistical Institute, Delhi (Jan 2026) · CMStats, London (Dec 2025) · NUS Singapore, IMS Young Mathematical Scientists Forum (Nov 2025)
Causal Inference Beyond Support
Yale Foundations of Data Science Colloquium, New Haven (Jan 2026)
Data Fusion for Partial Identification of Causal Effects
NeurIPS 2025, San Diego (Dec 2025)
A Cautionary Tale on Integrating Data with Disparate Outcomes
NeurIPS 2025, San Diego (Dec 2025)
Rashomon Set of Optimal Trees
Joint Statistical Meeting, Nashville (Aug 2025)
Machine Learning–Aided Causal Inference
University of Southern Denmark, DaCHE (2025)
Who Are We Missing? A Principled Approach to Identifying Underrepresented Groups
ACIC 2025, Detroit · ENAR 2025, New Orleans
Interpretable Machine Learning & Causal Inference for Advancing Healthcare and Public Health
Yale School of Public Health, New Haven (Jan 2025) · Johns Hopkins University, Applied Math & Statistics, Baltimore (Feb 2025) · Harvard University, Dept. of Statistics, Cambridge (Jan 2025) · Columbia University, Dept. of Biostatistics, New York (Jan 2025) · Boston University, Dept. of Biostatistics (Nov 2024) · University of Michigan, Dept. of Biostatistics, Ann Arbor (Nov 2024) · UT Austin, Information, Risk, and Operations Management (Nov 2024)
Integrating Multiple Datasets with Disparate Outcomes for Efficient Causal Inference
INFORMS 2024, Seattle (Oct 2024)
Characterizing Underrepresented Populations when Generalizing Experimental Evidence
ICHPS (Jan 2025) · IIT Gandhinagar (Nov 2024) · ACIC (May 2024) · NC State University (Mar 2024) · ENAR (Feb 2024) · ICERM (Nov 2023)
A Double Machine Learning Approach to Combining Experimental and Observational Studies
INFORMS Annual Meeting (Oct 2023) · IISA Annual Meeting (Dec 2022)
Causal Inference for High-Stakes Decision Making
Wake Forest University School of Medicine (Mar 2023) · NC State University (Jan 2023) · MIDAS, Johns Hopkins (Sep 2022) · Jacobs Technion-Cornell Institute (Oct 2022) · Microsoft Research (Nov 2022)
Validating Causal Inference Methods
ICML (Jul 2022) · Clinical Data Animation Center, MGH (Aug 2022) · SER Conference (Jun 2023)
Matching After Learning to Stretch
ICML (Aug 2023) · Duke Microeconometrics (Sep 2019) · IIT Gandhinagar (Dec 2019)
Effect of Epileptiform Activity in Critically Ill Patients
Clinical Data Animation Center, MGH (Sep 2021)
Teaching
Fall 2026
Instructor — Introduction to Data Science
Yale University
2024
Instructor — Interpretable Machine Learning Tutorial
International Conference on Computational Social Science (IC2S2)
Fall 2019
Instructor — Introduction to Causal Inference (Advanced)
Duke Datathon
Spring 2019
Teaching Assistant — COMPSCI 671 Machine Learning
Duke University
Fall 2018
Instructor — Introduction to Data Science
Duke MEMPDC (Consulting Club)
Fall 2018
Teaching Assistant — COMPSCI 590.02 Computational Microeconomics
Duke University
Spring 2018
Teaching Assistant — COMPSCI 223 Computational Microeconomics
Duke University
2016–2017
Teaching Assistant — COMPSCI 230 Discrete Mathematics
Duke University (Fall 2017, Spring 2017)
Fall 2016
Teaching Assistant — COMPSCI 201 Data Structures & Algorithms
Duke University
Popular Media
Harsh Parikh, Ankita Gupta, Subham. Covid-19: Mitigating the risk from reverse migration. Ideas for India, 2020
Harsh Parikh, Kumar Subham. Efficacy of India's Covid-19 response. Center for Soft Power, 2020
Ammar Malik, Harsh Parikh. Rents are driven by the quality of public services, not proximity to transit. Urban Wire: International Development, 2017
Fenohasina Rakotondrazaka Maret, Harsh Parikh, Rachel Wilder. Empowering women through international tourism. Urban Wire: International Development, 2017
Harsh Parikh. Book Review: The Indian Economy—A Macroeconomic Perspective. ARTNeT UNESCAP, 2017
Service
Reviewer
ICML (2026) · AISTATS (2021, 2023–26) · NeurIPS (2021, 2025) · Annals of Applied Statistics (2026) · JASA (2025) · JRSS-A (2025) · JRSS-B (2025) · JRSS (2021) · JMLR (2024) · PNAS (2024) · IISE Transactions on Healthcare Systems Engineering (2025) · Nature Human Behaviour (2022) · Management Science (2021–22)
Leadership
Project Manager, NC Voucher Program Evaluation (2017) · Project Manager, Slum Development, AINA IIT Delhi (2011–15) · Committee Chair, CS Dept. Socials, Duke (2019–20) · President, Duke Indian Students Association (2019–21) · Treasurer, Duke Cricket Team (2017–20)
Skills & Coursework
Coursework
Causal Inference, Machine Learning, Bayesian Statistics, Reinforcement Learning, Algorithms, Probability & Stochastic Processes, Linear Algebra, Real Analysis, Econometrics, Micro/Macroeconomics
Programming
Python, Java, C/C++, STATA, R, MATLAB, SQL, HTML, PHP, Perl, ArcGIS