Week-by-Week Outline

Below you will find a general outline of the topics we’ll cover in this course. All other materials regarding homework assignments, readings, and other information will be posted on the course website. Depending on how the class progresses, the order of these topics might change, so check regularly for an updated version of this outline:

Week Class Dates Topics
0 Aug 26 No class; complete initial assignment before week 1.
1 Aug 31 - Sep 2 Syllabus overview and course motivation; Multiple regression models: Quick review, comparing effect sizes and outliers.
2 Sep 7 – Sep 9 Multiple regression models (cont.): Statistical adjustment and collinearity; Correlation vs Causation.
3 Sep 14 – Sep 16 Introduction to Causal Inference: Potential outcomes framework, the fundamental problem of causal inference, causal estimands, and study design.
Homework 1 due
4 Sep 21 – Sep 23 Randomized-Controlled trials: The gold standard? How can we use RCTs to answer causal questions? Limitations of RCTs. Internal and external validity. A/B testing.
5 Sep 28 – Sep 30 Introduction to Observational Studies: Can we use data outside experiments to make causal claims?
Homework 2 due
6 Oct 5 – Oct 7 Observational studies: Selection on observables. Matching and its comparison to RCTs. Overt and hidden biases. Propensity score matching. How does matching compare to regression adjustment?
7 Oct 12 – Oct 14 Observational studies (cont.): Natural experiments and Differences-in-Differences.
Homework 3 due
8 Oct 19 – Oct 21 Observational studies (cont.): Regression discontinuity design (RDD). Introduction to prediction: What about prediction? Continuous and discrete responses using regressions. Overview of model checking.
Midterm exam due
9 Oct 26 – Oct 28 Building models: Bias-variance tradeoff; out-of-sample validation and cross-validation; Variable selection (e.g. stepwise selection).
10 Nov 2 – Nov 4 Model selection and regularization: Lasso and Ridge regression.
Homework 4 due
11 Nov 9 – Nov 11 Introduction to prediction models: K-nearest neighbors’ regression and classification.
12 Nov 16 – Nov 18 Prediction models (cont.): Classification and regression trees (CART).
Homework 5 due
14 Nov 30 – Dec 2 Prediction models (cont.): Random forests, boosting, and ensemble methods.
Homework 6 due
15 December 7 Final Exam due
15 December 12 Final Project due

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