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.
A geocomputational notebook to monitor subnational human development using Python. Besides exploratory data analysis, the notebook introduces geospatial mapping, spatial dependence, and spatial inequality.
Learn to map and monitor regional development across Bolivia with Python. The notebook shows how to explore the data, draw maps, and detect spatial patterns of inequality.