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Introduction to Causal Inference 

This course presents a general framework for causal inference:

  1. clear statement of the scientific question,

  2. definition of the causal model and parameter of interest,

  3. assessment of identifiability - that is linking the causal effect to a parameter estimable from the observed data distribution,

  4. choice and implementation of estimators including parametric and semi-parametric methods,

  5. interpretation of findings.


The estimation methods include G-computation, inverse probability of weighting (IPW), and targeted maximum likelihood estimation (TMLE) with Super Learner.


  • Developed with Dr. Maya Petersen of UC Berkeley

  • Winner of 2014 ASA’s Causality in Statistics Education Award - “individual or team that does the most to enhance the teaching and learning of causal inference in introductory statistics courses”

  • Course materials available at


G-computation methods


Inverse probability weighting


Super Learner (ensemble method)


Targeted maximum likelihood estimation


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