Week 09 - Causal Inference
To do before class
- Talk by Dan Graham (22min. video)
where he clearly explains the main issues about causality using
transport studies as the domain of application.
- Talk by George Davey Smith about causality in epidemiology
(30min. video). Some of the
concepts here are a bit advanced, but the examples are great.
- Chapter 9 of (Gelman & Hill, 2006) is a good introduction into causality
in regression models. Chapter 10 of the same book covers a bit more advanced
- (Angrist & Pischke, 2008) is one of the best treatments of causal inference
but is not entirely introductory. A gentler coverage of similar topics is
available on a more recent book (Angrist & Pischke, 2014).
- Angrist, J. D., & Pischke, J.-S. (2014). Mastering’metrics: The Path from Cause to Effect. Princeton University Press.
- Angrist, J. D., & Pischke, J.-S. (2008). Mostly harmless econometrics: An empiricist’s companion. Princeton university press.
- Gelman, A., & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge University Press.