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Causal Inference & Targeted Machine Learning is a Course

Causal Inference & Targeted Machine Learning

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Full course description

This course will introduce the Causal Roadmap, which is a general framework for Causal Inference: (i) clear statement of the research question, (ii) definition of the causal model and effect of interest, (iii) assessment of identifiability -  that is, linking the causal effect to a parameter estimable from the observed data distribution, (iv) choice and implementation of estimators, including state-of-the-art methods, and (v) appropriate interpretation of findings (Petersen & van der Laan, Epi, 2014). The statistical methods include G-computation, inverse probability weighting (IPW), and targeted minimum loss-based estimation (TMLE) with Super Learner, an ensemble machine learning method. The emphasis will be on practical implementation and real-world challenges and solutions.

 

This is an enhanced iteration of the causal inference course that was offered from July to September 2023.

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