policy evaluation

Introduction to the Synthetic Control Method in Python with mlsynth

How much did Proposition 99, the 1989 California tobacco program, reduce cigarette sales? This beginner Python tutorial teaches the synthetic control method, which builds a look-alike California from other states, finds about 19 fewer packs sold per person each year, and then tries hard to break that result. It comes with an interactive app that runs in your web browser.

Bayesian Spatial Synthetic Control in Python: California's Proposition 99 with scspill and mlsynth

Did the 1988 California tobacco law cut cigarette sales, and did it also affect neighboring states such as Nevada? This Python tutorial builds a look-alike California from other states in three steps, the last one letting nearby states be affected too, and finds that the drop in California holds up. It comes with an interactive app that runs in your web browser.

The Synthetic Control Ladder in Python: A Guided Tour of mlsynth on the Brexit Referendum

How much did the 2016 Brexit vote cost the British economy? This Python tutorial builds a look-alike United Kingdom from other wealthy economies with six related methods, finds output about 3 percent lower by the end of 2018, and shows how default settings can quietly change the answer. It comes with an interactive app that runs in your web browser.

From DiD to SDID: A Ladder of Synthetic Control Estimators, and What Brexit Cost the UK

How much did the 2016 Brexit vote cost the economy of the United Kingdom? We build a look-alike United Kingdom from other rich countries, step by step through a ladder of increasingly flexible methods, and compare what each step says. This R tutorial includes cheat sheets for Stata and Python and an interactive app that runs in your web browser.

Staggered Synthetic Difference-in-Differences (SDID) in Stata: Gender Quotas and Women in Parliament

Do gender quotas raise the share of women in national parliaments? In Stata, we compare 9 countries that adopted quotas in different years with look-alike groups built from 110 countries without quotas, and find an average gain of about 8 percentage points that varies a lot across countries. The tutorial includes an interactive app that runs in your web browser.

Synthetic Difference-in-Differences (SDID) in Stata: Re-evaluating California's Proposition 99

Did the 1988 tobacco tax in California reduce smoking? In Stata, we compare three ways of building a comparison for California from other states, including a newer method that blends their strengths, and all three agree that cigarette sales fell, though by different amounts. It comes with an interactive app that runs in your web browser.

Six Ways to Evaluate a Policy using R: Comparative Case Studies of Proposition 99

Did the 1988 California cigarette tax reduce cigarette sales, and do different methods agree on how much? This R tutorial applies six ways of evaluating a policy to the same data and finds that most point to a drop of roughly 13 to 20 packs per person each year. It comes with an interactive app that runs in your web browser.

Causal Machine Learning for Policy Evaluation: From ATE to IATE to a Better Assignment Rule

If a job training program helps unemployed people find work, who gains the most, and who should be offered a place first? This Python tutorial uses causal machine learning on simulated jobseeker data with known true effects to move from the average effect to personal effects and a better way to assign training. It comes with an interactive app that runs in your web browser.

The Synthetic Control Method in Stata: Did Proposition 99 Cut Smoking in California?

Did the 1988 tobacco tax and anti-smoking campaign in California cut cigarette sales? In Stata, we use the synthetic control method, which builds a look-alike California from a mix of other states, and find that sales fell by about 19 packs per person per year. It comes with an interactive app that runs in your web browser.