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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    

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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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