## General Links

syllabus

readings

a great econometrics textbook

notes on iterating expectations

notes on variance

R resources: [resource 1]

final exam sign-up sheet

## Lecture Slides

1 causal identification

– [Do you get “causation”? Test yourself.]

2 estimation and statistical inference for an ideal experiment

– [cov-adj-sim.R]

– [great blog post pt 1] [pt 2]

– [tight estimates of conditional variance]

3 linear regression and approximation inference

– [R code for examples]

4 regression and causal effects

5 notions of bias I (biased data)

6 notions of bias II (biased methods)

7 matching and weighting

– [a public service message on matching]

– [more on comparing matching methods]

8 robust inference I

– [useful blog posts: I II III IV V VI VII]

– [degrees of freedom adjustments for clustering & panel data]

9 robust inference II

– [R code for examples]

– [nice slides w/ bootstrap examples]

– [bootstrap dos and don’ts]

10 causal inference with instrumental variables I

– [how to think about IV when you’re confused]

– [IV then and now]

– [a lament on crappy IV]

– [don’t do IV] [do IV]

11 causal inference with instrumental variables II

– [how the Fulton Fish market worked]

– [what it looks like today]

– [classical simultaneous equations]

12 repeated observations I

– [R simulation]

– [“fixed effects” confusion blog discussion]

13 repeated observations II

– [nice triple diffs video]

14 regression discontinuity I

– [RD porn]

– [bandwidth selection primer]

– [are close elections RDDs confounded?]

15 regression discontinuity II

– [primer on kink design]

16 moderators, mediators, and causal explanation I

17 moderators, mediators, and causal explanation II

18 distributional effects and quantile regression

– [R code for censored quantile reg]

– [beyond Oaxaca-Blinder: quantile decompositions]

19 multiple outcomes and multiple testing

– [guidance on multiple comparisons adjustment]

– [multiple comparisons scenarios]

20 missing data and attrition

21 limited dependent variables I

22 limited dependent variables II

23 machine learning and causal inference

– [kernel trick]

– [hastie et al. textbook]

– [blog w/ good machine learning + causality posts]

24 generalization and interpretation

## Homework

Homework 1: assignment [data] [CLT notes with example simulation in R]

Homework 2: assignment

Homework 3: assignment [replication archive]

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

Homework 5: assignment [paper] [replication materials]

Homework 6: assignment [rep materials for #2]

Homework 7: assignment

Homework 8: assignment rep materials anes data