Part I: From causal questions to the statistical estimation problem   

A general roadmap for tackling causal questions

Pearl’s Structural Causal Model (SCM)    

Defining target causal quantities: Link between SCM and counterfactuals 

Defining the observed data and its link to the SCM 

Identifying causal effects    

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Part II: Statistical estimation and interpretation

Introduction to estimation    

Introduction to data-adaptive estimation and Super Learning    

Estimation of causal effects with data-adaptive methods

The propensity score and inverse probability weighting    

Inverse probability of treatment weighting (IPTW) for marginal structural model (MSM) parameters    

Introduction to Targeted Maximum Likelihood Estimation (TMLE)

TMLE examples, Interpretation, & Wrap-up

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Suggested citation for the course: 

M. Petersen and L. Balzer. Introduction to Causal Inference. UC Berkeley, August 2014. <>