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Monday 25 July – Friday 29 July 2022
2 hours of live teaching per day
12:30 – 14:30 CEST
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.
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.
3 credits Engage fully with class activities
4 credits Complete a post-class assignment
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.
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.
You must complete up to ten hours' preparatory work. This includes:
Becoming Familiar with R
Viewing pre-recorded lectures
