## General Links

- syllabus
- readings
- coding resources: [RMarkdown] [Stata Markdown]

## Lectures

- 1 causal identification
- 2 estimation and inference for a randomized experiment
- 3 linear regression and approximation inference
- 4 linear regression and causal effects
- 5 conditioning strategies for identifying causal effects
- 6 matching and weighting
- 7 robust inference I
- 8 robust inference II
- 9 instrumental variables I
- 10 instrumental variables II
- 11 repeated observations I
- 12 repeated observations II
- 13 regression discontinuity I
- 14 regression discontinuity II
- 15 mediators, moderators, and causal explanation I
- 16 mediators, moderators, and causal explanation II
- 17 distributional effects and quantile regression
- 18 multiple outcomes
- [guidance on multiple comparisons adjustments]
- [why you don’t need to adjust for all the tests in your life]
- [multiple comparisons scenarios]
- [inverse covariance weighting vs. factor analysis: pt. I pt. II]

- 19 missing data and attrition
- 20 limited dependent variables I
- 21 limited dependent variables II
- 22 machine learning and causal effects
- 23 generalization and interpretation

## Assignments

- homework 1:
- homework 2 (due 2/20 to the TAs):
- homework 3 (due 3/4 to the TAs):
- homework 4 (due 4/3 to the TAs):
- homework 5 (due 4/10 to the TAs):
- homework 6 (due 5/1 to the TAs):