A beginner-friendly tour of seven panel-data estimators, from pooled OLS to correlated random effects (Mundlak), applied to a two-period worker wage panel. Predict-first checks, two short proofs, an interactive lab, and worked exercises show why the within estimators nearly triple the union wage premium.
Understanding the Frisch-Waugh-Lovell theorem to isolate causal relationships by partialling-out confounders in a simulated fast-food coupon promotion, with an appendix that extends FWL to panel data
In June 1998 a 4.8-kilometre bridge over the Jamuna river connected 26 million isolated Bangladeshis to Dhaka and cut freight costs in half. This tutorial rebuilds the difference-in-differences evaluation of that bridge from the ground up in Python, using the Padma hinterland — a symmetric region left isolated by a river whose own bridge was not started until 2015 — as the comparison group. It teaches the 2x2 logic, parallel trends, two-way fixed effects, event studies and honest sensitivity analysis on satellite nighttime lights, then runs the same machinery over census employment shares, rice yields and a public-goods placebo. The two doubly robust estimators of the original paper are rebuilt by hand in NumPy and pushed through both diff-diff and pyfixest. All 122 published coefficients are audited side by side with the replication, and the defects found inside the shipped Stata package are documented in full.
A ground-up introduction to synthetic control in Python, built on the California Proposition 99 case study and climbing three stages: the classical simplex of Abadie, Diamond and Hainmueller; a Bayesian horseshoe prior that lets the data rather than a constraint choose the donors; and the Bayesian spatial model of Sakaguchi and Tagawa, which drops SUTVA on the donor pool and asks who else was treated. Every equation is derived and mapped to the code that implements it, using the scspill and mlsynth libraries. The answer for California survives every relaxation. The claim that the donor pool was clean does not.
A careful introduction to mlsynth, the Python library that puts the whole family of single-treated-unit synthetic control estimators behind one configuration interface. We climb the ladder from difference-in-differences to synthetic difference-in-differences with one mlsynth class per stage, showing what every option does and where the defaults will quietly hand you a different estimator. The case study is the 2016 Brexit referendum and what it cost UK GDP.
Climbing the ladder from difference-in-differences to synthetic difference-in-differences, one stage at a time, with every estimator hand-coded before it is run with its package. The case study is the 2016 Brexit referendum and what it cost UK GDP. Includes cheat sheets in R, Stata and Python.
Did a policy really work if it also affected the comparison regions? scspill, a Python package, estimates both the true effect and the spillover to them.
Reproducing Scott Cunningham's LaLonde test in Python — covariates rescue a difference-in-differences ATT only when they enter the control group's counterfactual trend, recovering the $1,794 experimental benchmark from a naive $3,621.
Are poorer regions catching up, and is inequality falling? geometrics answers with spatial methods in Python, Plotly figures, and no-code Streamlit apps.
What patterns and relationships hide in your panel data? expdpy reveals them in Python, with Plotly figures, publication tables, and no-code Streamlit apps.