Does job training help people find work when an important factor, such as past work experience, was never measured? Using simulated workers in Python, we compute a range that the true effect must lie within instead of a single number, and see why more data alone cannot narrow it. It comes with an interactive app that runs in your web browser.
Did a job training program raise the earnings of disadvantaged workers? This Python tutorial applies a four-step approach to cause and effect (state your assumptions, decide what to compare, estimate the effect and stress-test it) to the classic LaLonde job training data. It comes with an interactive app that runs in your web browser.
Does a cash bonus help unemployed workers find jobs faster? This Python tutorial uses double machine learning, which lets flexible prediction models strip out the influence of background characteristics, on data from a real experiment in Pennsylvania. It comes with an interactive app that runs in your web browser.
Can satellite images tell us how well each municipality in Bolivia is developing? This beginner-friendly Python tutorial trains a random forest, a machine learning model that averages many decision trees, tests it on places it never saw, and finds that the images hold real but limited information. It comes with an interactive app that runs in your web browser.
Does crime spill over from one neighborhood to the next? Using data on 49 neighborhoods in Columbus, Ohio, in Stata, we compare models in which crime, income and housing values in nearby areas also matter, and find that higher income lowers crime both in a neighborhood and next door. It comes with an interactive app that runs in your web browser.