Schedule
Lecture topics
The course is divided into three broad sections:
- Jan 7–16: Working with data
- Jan 21–Feb 6: Inference without models
- Feb 11 onward: Inference with models
Each date will be a link to the slides for that lecture.
| Date | Website Page | |
|---|---|---|
| Jan 7 | Welcome | |
| Asking questions | ||
| Setting up your computer | ||
| Jan 9 | Visualization | |
| Jan 14 & Jan 16 | Data transformation | |
| Thu, Jan 16 | Data transformation | |
| Tue, Jan 21 | Population sampling | |
| Thu, Jan 23 | Population sampling | |
| Tue, Jan 28 | Defining causal effects | |
| Thu, Jan 30 | Exchangeability and experiments | |
| Tue, Feb 4 | Conditional exchangeability | |
| Nonparametric estimation | ||
| Thu, Feb 6 | Directed Acyclic Graphs (DAGs) | |
| Tue, Feb 11 A and B | Why model? and What is a model? | |
| Thu, Feb 13 | Intro of outcome models for causal inference | |
| Tue, Feb 18 | With coding: Outcome models for causal inference | |
| Thu, Feb 20 | Logistic regression and its use in a causal outcome model | |
| Tue, Feb 25 | Model-based inverse probability weighting | |
| Thu, Feb 27 | Matching | |
| Tue, Mar 4 | Trees | |
| Thu, Mar 6 | Bootstrap for statistical uncertainty | |
| Tue, Mar 11 | Data-driven selection of an estimator | |
| Thur, Mar 13 | Course recap |
Assignments
| Due Date | Assignment |
|---|---|
| Fri, Jan 10 at 5pm | Problem Set 0 |
| Fri, Jan 17 at 5pm | Problem Set 1 |
| Fri, Jan 24 at 5pm | Peer Review 1 |
| Fri, Jan 31 at 5pm | Problem Set 2 |
| Fri, Feb 07 at 5pm | Peer Review 2 |
| Fri, Feb 14 at 5pm | Problem Set 3 |
| Fri, Feb 21 at 5pm | Peer Review 3 |
| Fri, Feb 28 at 5pm | Problem Set 4 |
| Fri, Mar 07 at 5pm | Draft Project Writeup |
| Mon, Mar 10 at 5pm | Project Slides |
| Fri, Mar 14 at 5pm | Project Writeup |