Introduction to Causal inference

How can causal relationships help with prediction?

Duration

10 to 20 h

Syllabus

Part I: Bayesian networks and causal inference (“Pearl causality”)

• Introduction to conditional independence
• Introduction to graphical models
• Do calculus and deriving causal inference from Bayesian networks
• Estimation of Bayesian networks from data
• Overview of current approaches to learning causal graphs from observational data

Part II: Potential outcomes and causal inference (“Rubin causality”)

• Intro to potential outcome terminology and notation
• Causal inference with randomized experiments
• Propensity scores and matching

Prerequisites

Solid background in Statistics/Econometrics

Credential

Forms part of Summer School Week II, part of Statistical Modelling and Inference MSc course, also taught in some Data Science Center workshops