This course provides an introduction to large-sample casual inference using standard techniques: regression (parametric and nonparametric), matching, weighting and doubly robust techniques, instrumental variables, fixed effects and difference-in-differences. The course also covers methods of statistical estimation used for these approaches.
This course introduces students to widely used procedures for regression analysis for descriptive and causal inference, and provides intuitive, applied, and formal foundations for regression and more advanced methods treated later in the major course sequence. The first half of the course covers descriptive inference with regression from a sampling perspective. The second half of the course covers causal inference with regression and standardization, also from a sampling and missing data perspective. The principles learned in this course provide a foundation for the future study of more... Read more about QTM 220 - Regression Analysis
Experiments are a prominent instrument of inquiry in the natural and the social sciences. The first part of the course introduces the logic of experimentation and discusses various methodological issues in the design and analysis of experiments. Topics include randomization inference, blocking, non-compliance, attrition, interference, and heterogeneous treatment effects. The second part of the course builds on this foundation to discuss some practical issues and ethical considerations in designing and implementing experiments.