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Causal Inference: Political Research Applications in R

Course Dates and Times

Monday 25 July – Friday 29 July 2022
2 hours of live teaching per day
12:30 – 14:30 CEST


Vikram Dayal

Institute of Economic Growth

Anand Murugesan

Central European University

Empirical research in politics relies on the ability to combine causal methods and to work with data. With its interactive design, this course introduces the key causal inference methods for examining issues in politics and public administration, using the statistical software R.

The course is designed for researchers, professional analysts, and students of impact evaluation. It will focus on practical research applications and is capped at a maximum of 16 participants to foster interaction and focus on the specific needs of each individual.  

Purpose of the course 

To give you an understanding of fundamental causal inference methods, and teach you how to implement them. You will learn how to use R to analyse and graph data.

ECTS Credits

3 credits Engage fully with class activities 
4 credits Complete a post-class assignment

Instructor Bio

Vikram Dayal is a Professor at the Institute of Economic Growth, Delhi.

He uses R to teach quantitative economics to diverse audiences. Vikram is the author of the Springer Brief An Introduction to R for Quantitative Economics: Graphing, Simulating and Computing.

He has published research on a range of environmental and developmental issues, from outdoor and indoor air pollution in Goa, India, to tigers and the invasive plant Prosopis juliflora in Ranthambore National Park.

Vikram studied economics in India and the USA. He received his doctoral degree from the Delhi School of Economics, University of Delhi.

Anand Murugesan is an economist at the Department of Public Policy, Central European University.

He combines insights from economics and related disciplines with causal inference tools – lab, lab-in-the-field, and observational data – to study social problems, particularly in developing countries.

Recent research includes studying the impact of religious violence on India's rural economy; how the practice of cash-for-votes corrodes democracies; and the long shadow of the Habsburg imperial history on tax compliance today.

Anand is coauthoring a book with Vikram Dayal entitled Demystifying Causal Inference: Public Policy Applications with R.

Twitter  @tapasiva

A substantial number of research questions involve making causal inferences. Do political reservations for women have an impact on policy decisions? Does decentralized governance improve social welfare? Could political term limits lower accountability to voters? 

This course will help you answer such questions with causal tools. We begin with a gentle introduction to R, followed by the potential outcomes and causal graph framework to build an understanding of cause. Topics include experiments (RCTs), matching, regression-discontinuity designs, and difference-in-differences.

Chapter 1 – Cause and Effect: Potential Outcomes and Causal Graphs
  • Dayal and Murugesan. Visualising cause and effect with DAGs [lecture notes] 
  • The Effect, Ch. 6 
Chapter 2 – Inference with Experiments
  • Dayal and Murugesan. Experiments [lecture notes] 
  • Imai, Quantitative Social Sciences (Ch. 2.4) 
  • The Effect, Ch. 9 
  • Case study: Political reservations in India
Chapter 3 – Matching
  • Dayal and Murugesan. Creating apples-to-apples with matching [lecture notes] 
  • The Effect, Ch. 13 
  • Case study: Decentralization for cost-effective conservation
Chapter 4 – Regression Discontinuity Design
  • Dayal and Murugesan. Causal inference with discontinuities [lecture notes] 
  • Quantitative Social Science (Ch. 4.3) 
  • Case study: Term limits and political accountability 
Chapter 5 – Difference-in-Differences
  • Dayal and Murugesan. Differencing out confounders [lecture notes] 
  • The Effect, Ch. 18 
  • Case study: German Reunification 
  • Course in inferential statistics or econometrics (or an equivalent course, eg Applied Regression Analysis)
  • Basic knowledge of R would also be useful. If you don't have this knowledge, consider taking the course  Introduction to R.

You must complete up to ten hours' preparatory work. This includes:

Becoming Familiar with R

  • Get Started with R (short note)
  • Quantitative Economics with R 

Viewing pre-recorded lectures

Each course includes pre-course assignments, including readings and pre-recorded videos, as well as daily live lectures totalling at least three hours. The instructor will conduct live Q&A sessions and offer designated office hours for one-to-one consultations.

Please check your course format before registering.

Online courses

Live classes will be held daily for three hours on a video meeting platform, allowing you to interact with both the instructor and other participants in real-time. To avoid online fatigue, the course employs a pedagogy that includes small-group work, short and focused tasks, as well as troubleshooting exercises that utilise a variety of online applications to facilitate collaboration and engagement with the course content.

In-person courses

In-person courses will consist of daily three-hour classroom sessions, featuring a range of interactive in-class activities including short lectures, peer feedback, group exercises, and presentations.


This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.