Discussion Assignments

Answers to assignments are available upon request. 

For two redacted real studies, apply the first steps of the roadmap to (i) specify the scientific question, (ii) represent knowledge with a SCM, and (iii) specify the target causal parameter. 

For the same studies, specify the observed data, assess identifiability, specify the statistical estimand, and discuss the needed positivity assumption. 

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R Labs

Defining the causal parameter and introduction to simulations in R    

Identifiability, linking the observed data to the causal model, and implementation of the simple substitution estimator based on the G-computation formula 

Cross-validation and data-adaptive methods for prediction

Inverse probability of treatment weighting (IPTW) estimators and the impact of positivity violations 

Targeted maximum likelihood estimation (TMLE)    

Inference with the non-parametric bootstrap and with influence curves for TMLE

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R Homework

Answers to R Assignments are available upon request. 

Defining the causal parameter & introduction to simulations in R    

Identifiability, linking the observed data to the causal model, and implementation of the simple substitution estimator based on the G-computation formula 

Cross-validation and data-adaptive methods for prediction    

Inverse probability of treatment weighting (IPTW) estimators and the impact of positivity violations 

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Final Project

Fully apply each step of the causal roadmap to a real-world problem

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Suggested citation for the course: 

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