Courses and Lectures

Core module at the intersection of econometrics and machine learning. Covers Double Machine Learning, causal forests, and doubly robust estimators with applications in labour, finance, and policy evaluation. Based on Breiman's two statistical cultures — reconciling prediction and causal identification.

A five-day in-person school at Churchill College, Cambridge — jointly taught with Professor Jeffrey Wooldridge (Michigan State University). Course 1: Causal Inference and Difference-in-Differences (Wooldridge). Course 2: Causal Inference and Machine Learning (Weeks). 20–24 July 2026. Register individually or for the full school.

A Cambridge executive programme for senior leaders with strategic responsibility for AI. Each delegate brings a real decision from their own organisation and works on it over three days, producing four short documents — a map of where in the firm AI matters most, an audit of one workflow, a design for human oversight, and a one-page board memo with a specific claim that could turn out to be wrong.

Public lecture on the economics of AI, labour, and expertise. The Three Tiers framework — Explicit Knowledge, Tacit but Inferred, and Beyond Human — and its implications for wages, skills, and policy. Presented at the Cambridge Festival of Ideas.