Semiparaemtric Inference
Semiparametric Efficiency Theory as Differential Calculus on a Space of Probability Distributions
Teaching
Integrating research and teaching within an intellectual community is one of the most rewarding aspects of academia. I view research and teaching as deeply interconnected endeavors. Research enables me to bring current ideas and discoveries into the classroom, while teaching challenges me to clarify concepts, explore new perspectives, and continue growing as a scholar.
Tutorial papers
The G-Formula
A Law of Iterated Expectation Primer for Causal Inference.
Graduate-level courses
BIOS 760R
Advanced Causal Inference
Graduate-level course on advanced causal inference from observational data, emphasizing graphical models and semiparametric efficiency theory. Topics include nonparametric identification in the presence of unmeasured confounding in DAGs with hidden variabels, influence-function based estimation, and mediation analysis.
Recipient of the Departmental Distinguished Teaching Award.
BIOS 761 / EPI 760
Causal Inference
Graduate-level course providing a comprehensive introduction to modern causal inference. Topics include potential outcomes, causal effect identification, graphical causal models, G-computation, inverse probability weighting, doubly robust estimation, and the integration of machine learning methods for causal effect estimation.
Short courses
Focused workshops and invited short courses.
Causal infernece . Python · Applied workflow
Hands-On Causal Inference with Python
Practice-oriented virtual short course introducing modern causal inference for data scientists and machine learning practitioners. Participants learn how to formulate causal questions, assess identifiability using causal graphs, and implement principled estimation strategies in Python using observational data.
Missing data · Graphical methods
Causal Graphical Methods for Nonignorable Missing Data
Short course on graphical approaches to nonignorable missing data, including representation, identification, sensitivity analysis, and practical strategies for empirical research. This short course has been thought at the American Causal Inference Conference (ACIC) and the conference on Uncertainty in Artificial Intelligence (UAI).
Fairness · Data science
Fairness in Data Science: Criteria, Algorithms and Open Problems
Short course introducing statistical and algorithmic notions of fairness, sources of bias in data-driven systems, causal perspectives on fairness, and open methodological challenges. This short course has been taught at the Joint Statistical Meetings (JSM) and the 16th annual Innovations in Design Analysis and Dissemination (IDAD).
Introductory causal inference
Should Susan Smoke: An Introduction to Causal Inference
An introductory course in causal inference focusing on building scientific skepticism and introducing students to philosophical and statistical notions of causality.
The course was featured in Johns Hopkins Hub Magazine.