Introduction to Causal Inference and the Causal Roadmap
With the 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 in Public Health: 1) clear statement of the research question, 2) definition of the causal model and effect of interest, 3) assessment of identifiability, 4) choice and implementation of estimators including parametric and non-parametric methods, and 5) appropriate interpretation of findings. The statistical methods include G-computation, inverse probability weighting (IPW), and targeted minimum loss-based estimation (TMLE) with machine learning.
Topics
- 1. Translate a research question and background knowledge into a causal model (directed acyclic graphs and non-parametric structural equation models). 2. Define the target causal parameter with counterfactuals. 3. Assess identifiability of the target causal parameter and express it as a parameter of the observed data distribution. 4. Understand the challenge posed by parametric estimation approaches and apply machine learning methods. 5. Understand the properties of and apply 3 estimators: G-computation, inverse probability weighting (IPW), and targeted minimum loss-based estimation (TMLE) with Super Learner. 6. Understand how to appropriately address missing outcomes, which may be differentially measured. 7. Apply course concepts to address cause-and-effect in a real data application. 8. Be ready to explore more advanced settings for Causal Inference.
Official catalogue course objectives and student learning outcomes
Prerequisites & course information
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Sources
- Course catalogue ↗2026-2027