Gov 51: Syllabus and Weekly Schedules

Sever Hall 103

Lecture and session attendances are mandatory. Participation (20%) is a significant part of learning for Gov 51 and we will try our best to make your time worth it. You can skip 1 lecture and 1 section without excuses. You can submit 1 problem set late without any excuses.

We will mainly reference QSS: An Introduction as our textbook. It is a great textbook for quantitative methods in social science. We will also read tutorials and chapters from other textbooks for certain topics.

Moreoever, Gov 51 will closely follow Gov 50. Gov 50 is the recommended (but not required) pre-requisite for this class. Please see a list of helpful resources by the amazing Prof. Matt Blackwell.

Causal Inference

[Week 1] Review

Reading: QSS Chapter 2 - Causality

[Week 2] Instrumental Variable

Reading: Instrumental Variables - an introduction \\ Mastering Metrics \\ Mostly Harmless Chapter 4 - IV and Causality (Optional)

[Week 3] Matching

Reading: Matching for Sample Balancing \\ Matching Estimators by Gary King (optional) \\ Matching Estimators (optional)

Problem Set I: 10% due on 2/15 11:59pm

Linear Regression and Prediction

[Week 4] Review

Reading: QSS Chapter 4 - Prediction
Slides: Lec 7 \\ Lec 8

[Week 5] Penalized Regressions and Variable Selection

Reading: Penalized Regression \\ Chapter 3: Regression Approaches \\ ISL Chapter 6 (First Edition) (Optional)
Slides: Lec 9 \\ Lec 10

[Week 6] Uncertainty and Inference

Reading: QSS Chapter 6 - Probability and Chapter 7 - Uncertainty

[Week 7] Midterm Exam: 20%

Slides: Lec 13
Review Session

[Week 8] Spring Break
[Week 9] Missing Data in Multiple Regression

Reading: Statistical Analysis with Missing Data - Chapter 1: Introduction \\ Missing Data by Gary King (Optional)

Machine Learning Methods

[Week 10] Text as Data: Bag of Words

Reading: Supervised machine learning for text analysis in R (Chapter 1 and 2) \\ NLP with Google (Ignore the coding part)

[Week 11] Text as Data: Unsupervised Learning

Reading: Unsupervised learning \\ Text and causal inference (optional) \\ Text preprocessing for unsupervised learning (optional)

[Week 12] Black Box Prediction Methods

Reading: Machine Learning by Andrew Ng (Skip the first 35 min during which he talks about logistics of the class.)

[Week 13] Network Analysis

Reading: QSS Chapter 5 - Discovery

Final Project and Poster Session

[Week 14] Final Project Poster Session: 30%

Computing

We’ll use R in this class to conduct data analysis. R is free, open source, and available on all major platforms (including Solaris, so no excuses). RStudio (also free) is a graphical interface to R that is widely used to work with the R language. You can find a virtually endless set of resources for R and RStudio on the internet. For beginners, there are several web-based tutorials. In these, you will be able to learn the basic syntax of R. We’ll post more R resources on the course website.

Accessibility

Harvard University values inclusive excellence and providing equal educational opportunities for all students. Our goal is to remove barriers for disabled students related to inaccessible elements of instruction or design in this course. If reasonable accommodations are necessary to provide access, please contact the Disability Access Office (DAO). Accommodations do not alter fundamental requirements of the course and are not retroactive. Students should request accommodations as early as possible, since they may take time to implement. Students should notify DAO at any time during the semester if adjustments to their communicated accommodation plan are needed.

Mental Health

College is a stressful time in one’s life and mixing it with a global pandemic, remote learning, and dislocation makes this one of the most fraught time any of us have probably faced. Please just get in touch if you are in need of support. Of course, there are other resources at Harvard if you need them. A few are listed below: