1.5 Objectives

  • Causal Analysis = ?6
  • See toc on the left!
  • Repeat basic concepts (‘distributional view of data’)
  • Discuss various identification strategies/designs
  • Replicate/reproduce exemplary studies with R
  • Gain visual understanding of data
  • Caveats.. NO fancy models.. no data management (a bit.. panel data) ..no scraping ..no proofs
  • Broad overview of different designs
  • Intro to “Causal thinking”!
  • Tools: R for analysis; Ggplot2/Plotly for data visualization (see coures script)

  1. Focuses on testing whether differences on one variable (e.g. income) are caused by differences on another variable (e.g. education) as opposed to simply being associated with differences on that other variable. This could be differences between individuals (e.g. Peter’s and Paul’s income as well as education) but also within individuals (e.g. Peter’s education and income at t1 and t2).↩