Courses

Core module at the intersection of econometrics and machine learning. Covers Double Machine Learning, causal forests, and double-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 board members and senior leaders — organised around the decision, not the technology. Delegates leave with six concrete artefacts including a Causal Decision Canvas and a working AI agent, each tied to a real decision in their own organisation.

Demonstration of agentic AI capability in a regulatory economics context — end-to-end analysis of 143,726 DSA moderation records using multi-model orchestration. A template for applied AI training in regulatory and policy organisations.

Public lecture on the economics of AI, labour, and expertise. The Three Tiers framework — from explicit knowledge to tacit inference to beyond-human discovery — and its implications for wages, skills, and policy. Presented at the Cambridge Festival of Ideas.

Proposal submitted to Cambridge University Press. Drawing on DS300 materials developed and tested at the Faculty of Economics. Covers Double Machine Learning, causal forests, double-robust estimators, and generalised random forests — with full R and Python implementations throughout.