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.
A Python package of synthetic control models that drop SUTVA on the donor pool — the treatment is allowed to reach the controls, and every model reports two estimands: the effect on the treated unit, purged of contamination, and the spillover received by each donor.
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.
A Python library to explore, analyze, and learn regional growth, convergence, and inequality — with explicit spatial methods, interactive Plotly figures and publication-quality tables, plus three no-code Streamlit apps (Explore, Analyze, Learn).
A Python library to explore, analyze, and learn panel data interactively — composable Plotly figures and publication-quality tables, plus three no-code Streamlit web apps (Explore, Analyze, Learn).
A comprehensive, beginner-friendly Python replication of Lessmann and Seidel (2017) — turning satellite nighttime lights into predicted regional GDP, building five population-weighted inequality indices from scratch, exploring the cross-country dynamics of regional inequality, and estimating the regional Kuznets curve, its determinants, and a Conley spatial-HAC robustness check with PyFixest.
Do industrial parks raise local economic activity — and for whom? A beginner's staggered difference-in-differences evaluation of Ethiopian industrial parks in Python, replicating Huang, Wang & Xu (2026) on synthetic calibrated data: TWFE and an event study with pyfixest, the modern Sun-Abraham, Borusyak/Gardner and Callaway-Sant'Anna estimators plus a Goodman-Bacon decomposition with diff-diff, survey-weighted repeated-cross-section DiD on DHS household welfare and women's empowerment, and Conley spatial standard errors.
How persistent is firm employment? Pooled OLS, fixed effects, Anderson-Hsiao IV, Arellano-Bond difference GMM, and Blundell-Bond system GMM on the classic 140-firm UK panel — and how the AR(2), Hansen, and instrument-collapse diagnostics separate the one defensible estimate from four seductive wrong ones.