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.

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Syllabus (2020)

Lecture notes

L1: Overview of the class

L2: Introduction to Stata and review of linear/OLS regression

L3: Causal inference

L4: Applied review of regression

L5: Regression adjustment and propensity scores (intro)

L6: Difference-in-difference models (intro)

L7: Regression discontinuity (intro)

L8: Maximum likelihood estimation (MLE)

L9: Marginal effects to interpret models

L11: GLM models and analysis of cost data

L12: Propensity scores and matching estimators

L13: Difference-in-difference designs (estimation)

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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