Causal Inference with Applications
STAT 286
Subject & Catalog Number
Course Information
Description
Substantive questions in empirical scientific and policy research are often causal. Does voter outreach increase turnout? Are job training programs effective? Can a universal health insurance program improve people's health? This class will introduce students to both statistical theory and applications of causal inference. As theoretical frameworks, we will discuss potential outcomes, causal graphs, randomization and model-based inference, sensitivity analysis, and partial identification. We will also cover various methodological tools including randomized experiments, regression discontinuity designs, matching, regression, instrumental variables, difference-in-differences, and dynamic causal models. The course will draw upon examples from political science, economics, education, public health, and other disciplines.
Recommended Prep
Stat 110, 111, and 139 or equivalent (probability, statistical theory, and linear models). Students who took Stat 186 in 2018 Fall and 2019 Fall should not enroll in this course.
Available for Harvard Cross Registration
NOTE: This course requires additional sections; you will be prompted to choose secondary components during the Add to Cart process
