StatML CDT Causal Inference Module
Graduate Course, University of Oxford, Department of Statistics, 2025
Course Overview
This course is a fast-paced overview of causal inference, equipping students with the tools and knowledge to analyze and interpret causal relationships from observational and experimental data.
Key Topics
- Potential Outcomes Framework: Introduction to the Rubin Causal Model.
- Randomization and Observational Studies: Design and analysis considerations.
- Confounding and Bias: Identification and mitigation strategies.
- Machine Learning: Overview of some statistically principled methods.
Material
Slides in PDF format:
Day 1
Day 2
Notes from an APTS module I teach also cover most of the material.
Data Sets
Resources
The R package causl can be obtained here, the vignette for plasmode simulation is updated here, and for heterogeneous treatment effects is here.
My Graphical Models lecture notes also contain some relevant material.