## General links

syllabus

readings

R resources: [basic intro] [online workshop] [another primer]

mahalanobis distance notes

iterating expectations notes

blog post on multivariate balance testing

notes on variance

## Lecture slides

1 identification

2 estimation & inference

3 regression mechanics

4 regression and causal effects

5 notions of bias I

6 notions of bias II

7 matching & weighting for causal effects

8 robust inference I (clustering)

9 robust inference II (bootstrapping)

10 instrumental variables I

11 instrumental variables II

12 repeated observations I

13 repeated observations II [r simulation code] [canonical example of RE vs. FE]

14 regression discontinuity I

15 regression discontinuity II

16 quantile regression [r code to make graphs]

17 multiple endpoints [r code for inv. cov. weighted index]

18 missing data

19 treatment effects with limited dependent variables I

20 treatment effects with limited dependent variables II

21 odds and ends

## Homework assignments

Homework 1: assignment data example simulation

Homework 2: assignment data

Homework 3: assignment

Homework 4: assignment dataset codebook R code for prep MatchIt manual MatchIt website

Homework 5: assignment rep materials for #1

Homework 6: assignment rep materials for #2 .do for complier gender

Homework 7: assignment rep materials forcing variable sorting test

Homework 8: assignment rep materials anes data