Applied Microeconometrics
Tools for credible causal inference in the real world
A practical guide to modern microeconometrics: experiments, quasi-experiments, machine learning, and everything in between.
Credible Causal Inference
How to go from data to identification using experiments, diff-in-diff, IV, RD, and more.
Hands-on Code
Replicable code call-outs in R, Stata, and Python. Explore the methods using real data.
Built for Applying
Written for applied work: More than 50 real papers with datasets, and code examples built for extending.
Modern Methods
Covers the frontier: penalised regression, double machine learning, causal forests, and marginal treatment effects alongside classical identification strategies.
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