[2017 Spring] POLS GA 1251 Quant II

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

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