Explore the foundations of causal graphical models in this comprehensive lecture from the Causality Boot Camp. Delve into key concepts including graphs, d-separation, and do-calculus as presented by Spencer Gordon from Caltech. Learn about causal graphical models, modified induced graphs, the back door criterion, and instrumental variables. Gain insights into the big picture of causality and its applications. Cover topics such as continuous variables, Bayesian networks, graph theory, and topological ordering. Enhance your understanding of causal inference and its mathematical underpinnings in this hour-long deep dive into the subject.
Introduction to Causal Graphical Models - Graphs, D-Separation, Do-Calculus
Overview
Syllabus
Introduction
Table of Contents
Graphs
Causal Graphical Models
Modified Induced Graph
The Back Door
Instrumental Variables
The Big Picture
Agenda
Continuous Variables
Bayesian Networks
Graph Theory
Bayesian Network
Topological Ordering
Taught by
Simons Institute
