Online Books

Quarto books on econometrics, causal inference, and applied research methods.

Foundations

Econometrics Guide R + Stata

A study guide to the estimators of applied econometrics — OLS, MLE, GLS, IV, GMM, and models for censored, discrete, count, panel, and survival data. 14 chapters, side-by-side R and Stata code.

Systematic treatment of causal inference

Introduction to Causal Econometrics with Observational Data R

A working guide to modern causal inference in R, organized in the order: identification, estimation, designs (DiD, IV, RDD, shift-share), longitudinal causal inference, survival, mediation, and causal discovery. 24 chapters.

Causal Econometrics with Julia Julia

The same ground in Julia, with several heavier estimators (TMLE on large samples, distributional DiD, Bayesian g-computation) where Julia is genuinely faster. Built alongside a stack of small Julia packages — CausalEstimate.jl, CausalGraphs.jl, Lavaan.jl, Crumble.jl, TASC.jl, and more — whose source is short enough to read. 27 chapters. Cross-linked with the R book.

Working notebook companion

Topics on Econometrics and Causal Inference R

A working notebook originally from my blogs: 48 chapters of posts written while reading, teaching, and consulting on applied econometrics. Each one stands alone.