RSS02.D11 Causal Inference with Natural Experiments: DiD, RDD, IV, & Matched Designs

This course is an introduction to causal inference for the social using observational data. We will discuss:

  • the nature of causal research,
  • how to design research to answer different types of causal questions,
  • how to analyze observational data to enable causal interpretations of phenomena
  • that were not randomized,
  • how to implement analysis using the R statistical language,
  • and how to interpret the results of causal analyses.

Specific topics will include:

  • potential outcomes,
  • ideal experiments,
  • observational matching,
  • sensitivity analysis,
  • instrumental variables,
  • discontinuity designs,
  • difference-in-differences,
  • multiple time period difference-in-differences designs,
  • synthetic controls,
  • and other special topics as permitted, including causal mediation and dynamic or time-varying treatments.


26 June 2023 - 30 June 2023
Course Fee

Regular: €995
Students & PhD's: €645

Early Bird Regular: €895 (application deadline* April 1st) 
Early Bird Students & PhD's: €580,50 (application deadline* April 1st)

Scholarships and discounts Find more information here
Application deadline

May 1st

*Your application is only completed when the course fee has been paid

Course leader Ryan T Moore
Level of participant
  • Advanced Bachelor
  • Master
  • PhD
  • PostDoc
  • Professional
Admission requirements ​Students should be familiar with introductory inferential statistics (such as tests of statistical significance), the rudiments of the linear model, and analysing data with software. The course will employ R as the language of analysis, but will support the interpretation of R code for those with background in Stata, SPSS, Python, or similar.
Admission documents
  • ​To get the student/PhD discount you need to upload a copy of your Student card or other proof of registration
  • If you are not a student/PhD, you can upload an empty document under 'Student Card'.
Mode of Study On Campus
ECTS 2 or 4 Find more information here
Location Radboud University