Übung Political Sociology: Applied Causal Inference

Course Details

Course Instructor: Nan Zhang (Office hours by appointment)

Course Website: https://nanzhang-polisci.github.io/QMPS_FSS25

Date and Time: Mondays, 13:45-15:15 in C 217 (A 5, 6 Bauteil C)

Course Description

This course will teach students how to analyze questions from the field of political sociology through the application of causal inference methods. We will begin by asking what it means for X to cause Y using the framework of potential outcomes. We will then look at some of the most popular research designs in causal analysis including experiments, regression discontinuity designs, difference-in-differences / two-way fixed effects, and instrumental variables.

Students will learn to apply these methods to real and simulated data in R.

The overarching goal is to provide students with the foundation to perform their own analyses (e.g. for their BA theses) by transferring the acquired skills to their research interests.

Course Structure and Requirements

Class-time will consist of a mix of lecture, “live-coding” exercises, reading discussion, group exercises, and student presentations of problem sets.

To encourage class attendance, I will only post lecture slides and code examples after we have covered the material in class.

In addition to in-class participation, students must complete the following assignments outside of class:

Reading memos.

Students are required to read applied research articles that implement the methods we cover in class and submit reading memos.

Your memos should consist of questions about the methods in the article (e.g. data analysis, the assumptions involved, etc.). There is no need to summarize of the readings. I will assume that you have read them.

You should work on the memos in your groups. That is, discuss the articles within your group beforehand, and then come up with a common set of questions that you would like to discuss in class.

There are 4 required reading memos in total. Your memos should be submitted to me via email by 8:00am on the following Fridays:

  • 21 Feb
  • 21 March
  • 2 May
  • 23 May

Your memos will be “graded” \(\checkmark\), \(\checkmark+\), \(\checkmark-\), and include some brief feedback on what you did well / how you can improve.

Take-home problem sets

Problem sets will provide students the opportunity to practice the methods they learn in class by analyzing real research data.

Students are required to work in groups. Here it’s important not to divide up the problem set between group members. Rather, all group members should work on all of the problems together.

There are 3 problem sets in total. Problem sets must be submitted to me via email by 23:59 on the following Sundays:

  • 6 April
  • 11 May
  • 4 July

I will randomly select students to present their problem set answers during our class sessions.

Your problem sets will be “graded” on a rough scale from A (“excellent”), B (“good”), C (“satisfactory”), and D (“unsatisfactory”). I will also provide some feedback on where you can improve.

If you still do not understand how to do something after we have discussed the problem set in class, please write me an email or make an appointment for office hours.

Attendance and Participation

Diligent preparation and active participation are essential for the success of this class! The material is hard, especially if you are encountering it for the first time, so don’t be afraid to make mistakes and ask questions! This is how you will learn.

To encourage participation from everyone, I will randomly select several students who are “on call” to answer questions during each class session. The idea is not to cause anyone stress or anxiety, but rather to “break the ice” and foster a more active environment where everyone can feel comfortable to participate.

If you are worried about this “cold calling” policy, please come see me.

Of course, situations may arise when you cannot attend class or did not have time to prepare. As a courtesy, please let me and group members know beforehand.

Assessment

Technically, the Prüfungsleistung is a Hausarbeit (final paper) due on 14 July 2025.

However, this course will graded somewhat differently than what you are used to from other classes. You will still be expected to complete assignments, and you will still receive a numeric grade on your transcript at the end of the semester. However, your grade will be determined based on your own self-assessment of your learning and effort in the class, with the possibility of adjustments up or down from me.

During the semester, you will be asked to complete two self-evaluation exercises (once at midterm, and once at the end of the semester). These exercises will include a series of questions about your work in the course. Specifically, you will be asked to reflect upon:

  • The amount of work you have done in the course. How much effort did you commit to:
    • the readings and memos
    • the group problem sets
    • participating in class discussions
  • Your successes and struggles in the course:
    • What did you learn each week?
    • What do you still not yet understand?
  • Opportunities for improvement:
    • What steps could you (or did you) take to improve your understanding of course concepts or increase your engagement in the class?
  • Propose the grade you feel you should receive in the course (up to that point), based on your:
    • class attendance and completion of all required assignments
    • your contributions to group work
    • your assignment grades
    • your overall comprehension and mastery of the course material
    • your improvement throughout the semester
  • Explain and justify why you would give yourself that grade.

When assigning final grades, I will strive to honor your end-of-semester assessment of your own performance and progress in this course. However, I reserve the right to alter your proposed grade as appropriate, based on my own evaluation of your performance and progress in the course as a whole. If such an alteration seems warranted, I will contact you to discuss your work in the course.

To help you keep track of your progress throughout the semester, it is helpful to keep a weekly “journal”. In particular, at the end of each class session, take 5 minutes and write down:

  1. What is one thing that you learned over the past week?
  2. What do you still do not yet understand?
  3. What steps will you (try to) take to improve your understanding?

Please include this journal as an appendix to your final reflection paper.

A Note on the Course Workload

This course is worth 6 ECTS. That’s 180 hours of “work” in total.

We will spend 21 of those hours in class. This means that you should work about 160 hours outside of class during the semester.

The course is set up so that you will be spending the majority of these 160 hours on group assignments.

I know that these assignments are time consuming.

For instance, not only will you have to read an academic article, but you will have to discuss it within your group and write up a joint response paper.

As a result, it will feel like you are doing a lot more work for this course in comparison to your other courses.

This is intentional. The assignments are designed so that you can learn not only from me, but also from each other.

On the other hand, you will not have to spend much time studying or preparing for the final exam. Again, this is intentional.

Rather than focusing on “cramming” at the end of the semester, I believe that you will learn more by working at a more constant pace for the entirety of the course.

Weekly Schedule

Note: the current version of the syllabus is a work in progress. In fact, it’s probably too much to cover in one semester. I will make adjustments, depending on the pace of the class, so check back often!

All changes to the syllabus will be announced in class.

Week 1 (10 Feb): Course Introduction

Nan will introduce the course, go over the syllabus, and answer any logistical questions you may have.

We will form groups for the remainder of the semester.

Throughout the course, we will use statistical simulations in R to illustrate statistical concepts.

To motivate this approach, we will end the session by simulating an answer to the famous Monty Hall problem.

Assignments for next week:

  • You should install both R and RStudio before our meeting next week. Make sure the software works on your laptops!

  • If you are new to R, please have a look at this excellent online material from Lion Behrens.

Week 2 (17 Feb): Probability and Statistics Review

We will review some basic concepts from probability and statistics that will come up again and again in the course.

To illustrate these concepts, we will use simulation to draw marbles from a large bowl.

This example will help us to understand the ideas of expected value, unbiasedness and uncertainty.

Assignment for next week:

  • Read chapter 1 of Angrist and Pischke’s book Mastering ’Metrics (you can skim the Appendix if you like).

  • Email me your first reading memo by 8:00am on Friday, 21 Feb. Don’t forget to put your name on your memo!

Week 3 (24 Feb): Potential Outcomes and Average Treatment Effects

We will start today by simulating a randomized experiment under the potential outcomes framework.

We will use this simulation to illustrate how randomization produces unbiased estimates of the average treatment effect (ATE).

No assignment this week.

Class cancelled: 3 March

Week 4 (10 March): Analyzing Experiments

Using simulations, we will relate the random variation in our simulated experimental results to the standard errors produced by t-tests and regression.

We will also work through how to construct confidence intervals.

Next, we will work through how to conduct randomization inference. This will (hopefully) provide a more intuitive explanation of p-values and the logic behind hypothesis testing.

Assignment for next week:

Class cancelled: 17 March

Week 5 (24 March): Analyzing Experiments (continued)

We will wrap up our discussion from last week and also discuss your questions about the assigned reading.

Assignment for next week:

  • Go through the lecture slides so far and come to class next week prepared with any R questions you have.

Week 6 (31 March): Data Visualization and R Review

We will use this session to review any outstanding questions about R you have.

Nan will also show you different ways to visualize your data.

  • Please complete Problem Set 1. Email me your group’s problem set answers by 23:59pm on Sunday, 6 April. Be prepared to present your answers in class next week.

Week 7 (7 April): Student Presentations

Students present their problem set solutions in class.

Assignment for next week:

  • Please fill out the midterm evaluation. You should write 2-3 pages max! Use your weekly “journals” to help jog your memory.

  • Please submit your evaluation by 8:00am on Friday, 11 April.

Easter Break (14 April - 25 April): No Class.

Week 8 (28 April): Regression Discontinuity

Nan will give a short presentation to introduce the logic of the regression discontinuity design (RDD).

We will spend the bulk of our time on data visualization, which is important for assessing the plausibility of the RDD.

Finally, if there is time, Nan will introduce some parametric estimation techniques.

Assignment for next week:

Week 9 (5 May): Regression Discontinuity (continued)

We will wrap up whatever we did not cover from last week, including both parametric and non-parametric estimation methods.

We will also discuss your reading questions.

Assignment for next week:

  • Please complete Problem Set 2. Email me your group’s problem set answers by 23:59pm on Sunday, 11 May. Be prepared to present your answers in class next week.

Week 10 (12 May): Student Presentations

We will discuss your answers to the problem set nd wrap up any outstanding questions from RDD.

No ssignment this week.

Week 11 (19 May): Introducing Instrumental Variables (IV)

Nan will give an introduction to instrumental variables (IV).

Assignment for next week:

Week 12 (26 May): Instrumental Variables (continued)

We will discuss your reading questions.

We will also look at some R code for how to estimate 2sls models.

Assignment for next week:

  • Please complete Problem Set 3. Email me your group’s problem set answers by 23:59pm on 4 July. Since we are already at the end of the semester, Nan will circulate the problem set answers.

Lecture Slides

You can find lecture slides (with code examples) here. Note that these will be posted only after we have covered the material in class.

References

If you want extra practice with R, check out the free ebook R for Data Science by Hadley Wickam.

If you are looking for more materials on causal inference, you can check out Andrew Heiss’ Program Evaluation class, or this excellent ebook by Nick Huntington-Klein: theeffectbook.net.

Both resources are free, provide code examples, and are accompanied by youtube videos!

Finally, StatQuest by Josh Starmer has an excellent set of youtube videos reviewing basic statistical concepts and linear regression.