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This is a seminar course. By reading and discussing an introductory book as well as research papers about computational social science, students will become familiar with core issues and techniques in the field.


Data collected through digital systems, such as online social networks, search engines, mobile phones, apps, etc., offer great opportunities for addressing important research questions about individual as well as collective human behavior. Whereas such issues had previously been studied primarily by social scientists, the sheer size of modern social data sets, as well as the fact that they are produced within computational systems, requires computational ways of thinking about, and processing, them.

The goal of this seminar is to acquaint students with some of the fundamental questions and techniques arising in the context of computational social science, with a focus on eliciting causal links from observational data.

We will explore the above topics simultaneously in two ways:

We will alternate between these two kinds of readings: every week, we will discuss either a portion of the book or a research paper related to the previously discussed portion of the book. All students will write a short summary and review of the respective reading, and one student will lead the in-class discussion about it. Beyond familiarizing themselves with research in the field, students will become better at assessing and critiquing scholarly work (by discussing and reviewing papers).

Through this course, students will familiarize themselves with some of the key methods used in computational social science and will see them applied in concrete studies. Moreover, they will increase their ability to summarize and critique scientific papers.

Previous years: 2022, 2021, 2020, 2019, 2018, 2017.


  • Coursebook listing
  • Time: Tuesays, 10:15 – 12:00, 21 Feb – 30 May 2023
  • Format: This is an in-person seminar, to be held in INM201.
  • Reviews are to be submitted via EasyChair by Sunday 23:59
  • Everybody is welcome to join us as a guest in reading and discussing the papers and book chapters listed below (for as many or as few papers as they like)!


  • In weeks markedΒ πŸ“– we discuss book chapters from The Effect. In weeks markedΒ πŸ“, we discuss a research paper.
  • It’s key that everyone must read the respective week’s material ahead of time, so we can have a deep, meaningful discussion.
  • Discussion leaders guide a discussion about the respective week’s material (and any additional material they deem worthy of talking about).
  • To make things easier, here are all papers in a single zip file.

Week 1 (21 Feb)

Introduction and logistics [slides]

πŸ“– Week 2 (28 Feb): Research Design

Discussion leader: Francesco Salvi

The Effect, chapters 1–5

πŸ“– Week 3 (7 Mar): Causality

Discussion leader: Dorothee Beckendorff

The Effect, chapters 6–11

πŸ“ Week 4 (14 Mar): Causality

Discussion leader: Catalina Álvarez

πŸ“– Week 5 (21 Mar): Regression

Discussion leader: Johann Kammholz

The Effect, chapters 12–13

πŸ“ Week 6 (28 Mar): Regression

Discussion leader: Laurynas Lopata

πŸ“– Week 7 (4 Apr): Matching

Discussion leader: Edvin Maid

The Effect, chapter 14

Week 8 (11 Apr)

No meeting (Easter week)

πŸ“ Week 9 (18 Apr): Matching

Discussion leader: Vamsi Nallapareddy

πŸ“– Week 10 (25 Apr): Simulation, Fixed Effects

Discussion leader: Giuseppe Russo

The Effect, chapters 15–16

Week 11 (2 May)

No meeting

πŸ“– Week 12 (9 May): Events, Difference-in-Differences

Discussion leader: Manoel Horta Ribeiro

The Effect, chapters 17–18

πŸ“ Week 13 (16 May): Events, Difference-in-Differences

Discussion leader: Bhargav Srinivasa Desikan

πŸ“– Week 14 (23 May): Instruments, Discontinuities

Discussion leader: Venia Veselovsky

The Effect, chapters 19–20

πŸ“ Week 15 (30 May): Instruments, Discontinuities

Discussion leader: Ankita Singhvi