30215
Advanced seminar
WiSe 20/21: If, when, why, what? Causality and causal inference
Brzinsky-Fay & Ohr
Additional information / Pre-requisites
Besides interest in the exciting topic, it is necessary that participants have some knowledge in statistics and applied data analysis, preferably in SPSS and/or Stata.
Comments
Causal inference has undergone a major transformation; some even perceive a “causal revolution” (Judea Pearl). In the first part of the seminar, we seek to carefully discuss the main notions and instruments of this new way to think about causality and causal inference (e.g., back-door adjustment, instrumental variables, analysis of counterfactuals). Causal models, represented in particular as causal diagrams, will be in the center of our discussion. In the second part of the seminar, we will examine how these notions and instruments of (new) causal inference can be applied in practical quantitative research. We will cover identification strategies such as instrumental variables, difference-in-difference approaches and regression discontinuity designs as well as estimation methods such as matching. close
Suggested reading
- Pearl, J., Glymour, M., and Jewell, N. (2016): Causal Inference in Statistics: A Primer. Wiley: New York, NY.
- Pearl, J., and MacKenzie, D. (2018): The Book of Why: The New Science of Cause and Effect. Basic Books: New York, NY.
15 Class schedule
Regular appointments
Mon, 2020-11-02 10:00 - 12:00
Mon, 2020-11-09 10:00 - 12:00
Mon, 2020-11-16 10:00 - 12:00
Mon, 2020-11-23 10:00 - 12:00
Mon, 2020-11-30 10:00 - 12:00
Mon, 2020-12-07 10:00 - 12:00
Mon, 2020-12-14 10:00 - 12:00
Mon, 2021-01-04 10:00 - 12:00
Mon, 2021-01-11 10:00 - 12:00
Mon, 2021-01-18 10:00 - 12:00
Mon, 2021-01-25 10:00 - 12:00
Mon, 2021-02-01 10:00 - 12:00
Mon, 2021-02-08 10:00 - 12:00
Mon, 2021-02-15 10:00 - 12:00
Mon, 2021-02-22 10:00 - 12:00