This is the new version my Health Services Methods I class (starting in the 2020 Fall semester). This class is an introduction to regression models (linear, logistic, probit, GLMs) and research designs for observational data (regression adjustment/propensity scores, difference-in-difference, regression discontinuity, instrumental variables, longitudinal data). We will practice model interpretation until it becomes (almost) second nature. The old version of this class was an introduction to regression modeling.
Please note the copyright notice below. Most of the material on this site will be part of a book on statistics/econometric methods to be published by Cambridge University Press. Read more about it at perraillon.com. Happy to receive feedback.
L3: Causal inference
L5: Regression adjustment and propensity scores (intro)
L6: Difference-in-difference models (intro)
L7: Regression discontinuity (intro)
L8: Maximum likelihood estimation (MLE)
L14: Regression discontinuity designs: rdrobust, sharp and fuzzy RDD, and intro to instrumental variables. This has a comparison of parametric with nonparametric rdrobust, which, given a bandidth h, it's really a kernel-weighted parametric method (see slides 59 and 60). The data-driven optimal bandwidth is the more interesting part.
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