Syllabus
Instructor
Dr. Andrew Heiss
357 Andrew Young School
aheiss@gsu.edu
@andrewheiss
Office hours: Sign up here.
E-mail is the best way to get in contact with me. I will try to respond to all course-related e-mails within 24 hours (really), but also remember that life can be busy and chaotic for everyone (including me!), so if I don't respond to your e-mail right away, don't worry!
Course
Mondays
August 26–December 9, 2019
4:30–7:00 PM
Langdale Hall 127
Slack
Course objectives
By the end of this course, you (1) will be literate in the language of causal inference, (2) will communicate evaluation outcomes clearly, and (3) will understand the ethics and limits of data analysis by designing, critiquing, coding, and running rigorous, valid, and feasible evaluations of public sector programs focused on society’s most pressing problems.
Specifically, you’ll be able to:
- Explain the philosophy of causation
- Identify and diagram program logic models
- Outline theories of change with directed acyclic graphs (DAGs)
- Summarize key threats to causal inference, identify these threats in evaluations, and mitigate these threats with research design
- Develop rigorous and valid statistical measures
- Run statistical models
- Explain the theory, research design, methods, and results of evaluations to all types of stakeholders, from highly trained econometricians to the general public
- Share your analyses and data with the public
- Identify ethical issues and limits in data science and program evaluation
- Become curious and confident in consuming and producing evaluations
Course philosophy
Classical statistics classes spend substantial time covering probability theory, null hypothesis testing, and other statistical tests first developed hundreds of years ago. Some classes don’t use software or actual real data and instead live in the world of mathematical proofs. They can be math-heavy and full of often unintuitive concepts and equations.
In this class, we will take the opposite approach. We begin with data and learn how to tidy, wrangle, manipulate, and visualize it with code. Later in the semester we’ll turn to the powerful toolbox of causal inference approaches, but continue to keep the focus on data as we do so.
In other words, there’s way less of this:
\[ f(x) = \dfrac{1}{\sqrt{2\pi}} e^{-\frac12 x^2} \]
And way more of this:
summary_monthly_temp <- weather %>%
group_by(month) %>%
summarize(mean = mean(temp),
std_dev = sd(temp))Over the last decade there has been a revolution in statistical and scientific computing. Open source languages like R and Python have overtaken older (and expensive!) corporate software packages like SAS and SPSS, and there are now thousands of books and blog posts and other online resources with excellent tutorials about how to analyze pretty much any kind of data.
This class will expose you to R—one of the most popular, sought-after, and in-demand statistical programming languages. Armed with the foundation of R skills you’ll learn in this class, you’ll know enough to be able to find how to analyze any sort of data-based question in the future.
Pep talk!
Learning R can be difficult at first—it’s like learning a new language, just like Spanish, French, or Chinese. Hadley Wickham—the chief data scientist at RStudio and the author of some amazing R packages you’ll be using like ggplot2—made this wise observation:
It’s easy when you start out programming to get really frustrated and think, “Oh it’s me, I’m really stupid,” or, “I’m not made out to program.” But, that is absolutely not the case. Everyone gets frustrated. I still get frustrated occasionally when writing R code. It’s just a natural part of programming. So, it happens to everyone and gets less and less over time. Don’t blame yourself. Just take a break, do something fun, and then come back and try again later.
If you’re finding yourself taking way too long hitting your head against a wall and not understanding, take a break, talk to classmates, ask questions in Slack, e-mail me, etc.
I promise you can do this.
Course materials
Most of the readings in this class are free.
Books
We will only use one physical textbook. There are three official textbooks for the class:
- Paul J. Gertler et al., Impact Evaluation in Practice, 2nd ed. (Inter-American Development Bank; World Bank, 2016), https://openknowledge.worldbank.org/handle/10986/25030.Free!
- Scott Cunningham, Causal Inference: The Mixtape, 2018, https://www.scunning.com/mixtape.html.Also free!
- Joshua D. Angrist and Jörn-Steffen Pischke, Mastering ’Metrics: The Path from Cause to Effect (Princeton, NJ: Princeton University Press, 2015).≈$25 used, ≈$30 new at Amazon
The World Bank’s Impact Evaluation in Practice will be our main textbook. It’s written at a general, easy-to-understand level with relatively minimal math. Mastering ’Metrics goes into more depth about the mechanics of different causal inference approaches and has a bit more math, but it’s still fairly accessible. Causal Inference: The Mixtape has even more math, but hopefully not an excessively terrifying amount.
You do not need to understand all the equations and notation. If your eyes start to gloss over the Greek letters and subscripts, it’s okay. Try to learn them, but don’t stress out about it too much.
Articles, book chapters, and other materials
There will also occasionally be additional articles and videos to read and watch. When this happens, links to these other resources will be included on the reading page for that week.
R and RStudio
You will do all of your analysis with the open source (and free!) programming language R. You will use RStudio as the main program to access R. Think of R as an engine and RStudio as a car dashboard—R handles all the calculations and the actual statistics, while RStudio provides a nice interface for running R code.
R is free, but it can sometimes be a pain to install and configure. To make life easier, you can (and should!) use the free RStudio.cloud service, which lets you run a full instance of RStudio in your web browser. This means you won’t have to install anything on your computer to get started with R! We will have a shared class workspace in RStudio.cloud that will let you quickly copy templates for labs and problem sets.
RStudio.cloud is convenient, but it can be slow and it is not designed to be able to handle larger datasets or more complicated analysis. Over the course of the semester, you’ll probably want to get around to installing R, RStudio, and other R packages on your computer and wean yourself off of RStudio.cloud. This isn’t necessary, but it’s helpful.
You can find instructions for installing R, RStudio, and all the tidyverse packages here.
Online help and Slack
Data science and statistical programming can be difficult. Computers are stupid and little errors in your code can cause hours of headache (even if you’ve been doing this stuff for years!).
Fortunately there are tons of online resources to help you with this. Two of the most important are StackOverflow (a Q&A site with hundreds of thousands of answers to all sorts of programming questions) and RStudio Community (a forum specifically designed for people using RStudio and the tidyverse (i.e. you)).
Additionally, we have a class chatroom at Slack where anyone in the class can ask questions and anyone can answer. Ask questions about the readings, lectures, or problem sets on Slack. I will monitor it regularly, and you should also all do so as well. You’ll likely have similar questions as your peers, and you’ll likely be able to answer other peoples’ questions too.
Course policies
Be nice. Be honest. Don’t cheat.
We will also follow Georgia State’s Code of Conduct.
Course evaluation and evolution
This syllabus reflects a plan for the semester. Deviations may become necessary as the semester progresses.
Because this is a new class (and because this is the first time this course has been taught with R at the Andrew Young School), there will be inevitable bumps along the way. Please be patient as we get everything ironed out!
I’d love your help to help improve the class as we go. To facilitate this, I have a couple requests:
At the end of every class, there will be a link to an anonymous Google Form with a few quick questions asking about the clearest and muddiest things from that day. Please fill this out regularly. It will be hard to remember, since we get out so late, but it’s extraordinarily helpful for me.
At some point in the middle of the semester, someone from GSU’s Center for Excellence in Teaching and Learning (CETL) will come and run a 25 minute Group Instructional Feedback Technique (GIFT) session, where I’ll step out and you’ll all talk to them about the class. Don’t worry—the people from CETL are super nice :)
Also, please take the time to fill out the official GSU course evaluation at the end of the semester!
Office hours
Please watch this video:
Office hours are set times dedicated to all of you. This means that I will be in my office (wistfully) waiting for you to come by with whatever questions you have. This is the best and easiest way to find me outside of class and the best chance for discussing class material and concerns. Please come by!
Outside of regularly scheduled office hours, you can easily make an appointment with me online.
This can be a difficult class. Do not suffer in silence! Come talk to me!
Class conduct and expectations
On the first day of class, will come up with rules, expectations, and policies regarding laptop use and other issues. Those will be listed here.
Laptops
This is a computer-heavy course and each class session will require extensive laptop use. Occasionally I may ask that laptops be closed for some in-class activities, but in general you will be expected to use your computer. Use your computer responsibly in class.
Counseling and Psychological Services (CPS)
Life at GSU can be complicated and challenging. You might feel overwhelmed, experience anxiety or depression, or struggle with relationships or family responsibilities. Counseling and Psychological Services (CPS) provides free, confidential support for students who are struggling with mental health and emotional challenges. The CPS office is staffed by professional psychologists who are attuned to the needs of all types of college and professional students. Please do not hesitate to contact CPS for assistance—getting help is a smart and courageous thing to do.
Basic needs security
If you have difficulty affording groceries or accessing sufficient food to eat every day, or if you lack a safe and stable place to live, and you believe this may affect your performance in this course, please contact the Dean of Students for support. They can provide a host of services including free groceries from the Panther Pantry and assisting with homelessness with the Embark Network. Additionally, please talk to me if you are comfortable in doing so. This will enable me to provide any resources that I might possess.
Lauren’s Promise
I will listen and believe you if someone is threatening you.
Lauren McCluskey, a 21-year-old honors student athlete, was murdered on October 22, 2018 by a man she briefly dated on the University of Utah campus. We must all take action to ensure that this never happens again.
If you are in immediate danger, call 911 or GSU police (404-413-3333).
If you are experiencing sexual assault, domestic violence, or stalking, please report it to me and I will connect you to resources or call GSU’s Counseling and Psychological Services (404-413-1640).
Any form of sexual harassment or violence will not be excused or tolerated at Georgia State. GSU has instituted procedures to respond to violations of these laws and standards, programs aimed at the prevention of such conduct, and intervention on behalf of the victims. Georgia State University Police officers will treat victims of sexual assault, domestic violence, and stalking with respect and dignity. Advocates on campus and in the community can help with victims’ physical and emotional health, reporting options, and academic concerns.
Academic honesty
Violation of GSU’s Policy on Academic Honesty will result in an F in the course and possible disciplinary action.So seriously, just don’t cheat or plagiarize!
All violations will be formally reported to the Dean of Students.
Special Needs
Students who wish to request accommodation for a disability may do so by registering with the Office of Disability Services. Students may only be accommodated upon issuance by the Office of Disability Services of a signed Accommodation Plan and are responsible for providing a copy of that plan to instructors of all classes in which accommodations are sought.
Students with special needs should then make an appointment with me during the first week of class to discuss any accommodations that need to be made.
Assignments and grades
You can find descriptions for all the assignments on the assignments page.
| Assignment | Points | Percent |
|---|---|---|
| Preparation (≈ 15.5 × 14) | 215 | 21.8% |
| Problem sets (8 × 40) | 320 | 32.5% |
| Code-through | 50 | 5.1% |
| Exam 1 | 100 | 10.2% |
| Exam 2 | 100 | 10.2% |
| Final project | 200 | 20.3% |
| Total | 985 | — |
| Grade | Range | Grade | Range |
|---|---|---|---|
| A | 93–100% | C | 73–76% |
| A− | 90–92% | C− | 70–72% |
| B+ | 87–89% | D+ | 67–69% |
| B | 83–86% | D | 63–66% |
| B− | 80–82% | D− | 60–62% |
| C+ | 77–79% | F | < 60% |
Cute animals
Once you have read this entire syllabus and the assignments page, please click here and e-mail me a picture of a cute non-feline, non-canine animal. For real. Brownie points if it’s animated.