Riddhi man Adib, Ph.D.

I am currently a Research Scientist III, in the Data and Analytics Division within the Epidemiological Research Section, at the Center for Health Care Quality, California Department of Public Health. I am also a Marquette alumnus, an ex-BUETian, and an ex-cadet.

I have 5+ years of research experience in Machine Learning & Data Science for healthcare; as well as software development & engineering. I have had 15+ publications and service as reviewer in 10+ conferences and journals in the Healthcare and Machine Learning domain. Currently, I am actively working in the field of Causal Inference; my primary focus is to apply Causal Inference and related theories in exploring cause-and-effect relationships in experimental and observational datasets.

My research goals aim to achieve data-driven causal explorations, mostly for Electronic Healthcare Data My primary tools are Structural Causal Models, Propensity Score Matching, A/B Testing and a little bit of Do-Why.

Outside research activities, I destress mostly by singing. My other hobbies include playing guitar, reading thriller books, playing board games, PS5 games & basketball, and some more.

The contents, including all opinions and views expressed or implied, are entirely personal and do not necessarily represent the opinions or views of any other person or organization including the California Department of Public Health. The California Department of Public Health has not reviewed or approved, and is not responsible for, the material contained in this profile.

selected publications

PMLR

A Causally Formulated Hazard Ratio Estimation through Backdoor Adjustment on Structural Causal Model

Riddhiman Adib, Paul Griffin , Sheikh Iqbal Ahamed , and 1 more author

In Machine Learning for Healthcare Conference , 2020

Abs

Identifying causal relationships for a treatment intervention is a fundamental problem in health sciences. Randomized controlled trials (RCTs) are considered the gold standard for identifying causal relationships. However, recent advancements in the theory of causal inference based on the foundations of structural causal models (SCMs) have allowed the identification of causal relationships from observational data, under certain assumptions. Survival analysis provides standard measures, such as the hazard ratio, to quantify the effects of an intervention. While hazard ratios are widely used in clinical and epidemiological studies for RCTs, a principled approach does not exist to compute hazard ratios for observational studies with SCMs. In this work, we review existing approaches to compute hazard ratios as well as their causal interpretation, if it exists. We also propose a novel approach to compute hazard ratios from observational studies using backdoor adjustment through SCMs and do-calculus. Finally, we evaluate the approach using experimental data for Ewing’s sarcoma.
AI in Med

Structural causal model with expert augmented knowledge to estimate the effect of oxygen therapy on mortality in the icu

Md Osman Gani , Shravan Kethireddy , Riddhiman Adib, and 3 more authors

Artificial intelligence in medicine, 2023