Introduction to Causal inference

How can causal relationships help with prediction?

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

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Data Sciencce Center Barcelona Graduate School of Economics Ramón Trías Fargas, 25-27 08005 Barcelona, Spain.

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