Jermain C. KAMINSKI

Abstract

This workshop introduces causal inference applications using Structural Causal Models (SCMs) and Directed Acyclic Graphs (DAGs). In the first half, participants will learn about the basics of Do-calculus, the critical role of mediators, confounders, and colliders, and explore the rising significance of causal machine learning in industry practice. Further, Causal Discovery (CD) and Large Language Models (LLM) will demonstrate how causal relationships in observational data can be supported with algorithm-supported induction. The second half of the workshop is dedicated to practical applications, wherein attendees will gain hands-on experience with the DoWhy library, running Python code on CoLab.