Intro to Causal Inference

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UMass Amherst
With the recent and ongoing 'data explosion', methods to delineate causation from correlation are perhaps more pressing now than ever. This course will introduce 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, 4) choice and implementation of estimators including parametric and semi-parametric methods, and 5) interpretation of findings. The methods include G-computation, inverse probability of treatment weighting (IPTW), and targeted maximum likelihood estimation (TMLE) with Super Learning, an ensemble machine learning method. Students gain practical experience implementing these estimators and interpreting results through discussion assignments, R labs, and R assignments.
Requirements: A course in intermediate biostatistics (e.g. BIOSTAT640) and experience with regression modeling (e.g. BIOSTAT650); or instructor permission. Recommended: a course in intermediate epidemiology (e.g. EPI632, EPI737)
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Permission is required for interchange registration during the add/drop period only.
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Course Sections

Intro to Causal Inference
Sect # Credits Instructor(s) Instructor Email Meeting Times Location
01 3.0 Laura Balzer M W 2:30PM 3:45PM Arnold Room 101