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.

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).

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).

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.