Topics on econometrics and causal inference

Preface

This is a working notebook rather than a textbook. The chapters are posts I wrote while reading, teaching, and consulting on applied econometrics, often because a colleague or a student asked a question that did not have a tidy answer in the books I had. Each one stands on its own. The order did not follow a plan, and the same topic sometimes appears more than once as my understanding changed.

The common question is the one Angrist and Pischke emphasize: what is being identified, by what variation, under what assumptions. The posts ask that question in different settings: interactions and marginal effects, fixed and correlated random effects, matching and weights, difference-in-differences, synthetic control, count models, TMLE and AIPW, mediation, proximal causal inference, partial interference, conjoint and list experiments, uplift modeling, and policy trees.

Most examples are in R. I use Stata where it is more natural, and sometimes Python or Julia when the package is better there. The two companion books, Introduction to Causal Econometrics and Causal Econometrics with Julia, organize the same material more systematically. This collection is the messier version: useful when I want to see how a specific method behaves on a specific problem.

How to cite

Ao, Xiang. Topics on Econometrics and Causal Inference. https://xiangao.github.io/blog_book/.

@book{ao_topics_econometrics,
  author = {Ao, Xiang},
  title  = {Topics on Econometrics and Causal Inference},
  year   = {2026},
  url    = {https://xiangao.github.io/blog_book/}
}