6  Causality

6.1 R packages needed for this chapter

library(tidyverse)
library(ggdag)  # optional  

6.2 Intro to causality

Check out this talk.

6.3 Literature

Rohrer (2018) provides an accessible introduction to causal inference. Slightly more advanced is the introduction by one of the leading figures of the Field, Judea Pearl, Pearl et al. (2016). If your after a text book on modelling that covers causal inference, and if you like Bayesian statistics, than you should definitely check out McElreath (2020).

McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan (2nd ed.). Taylor and Francis, CRC Press.

Pearl, J., Glymour, M., & Jewell, N. P. (2016). Causal inference in statistics: A primer. Wiley.

Rohrer, J. M. (2018). Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data. Advances in Methods and Practices in Psychological Science, 1(1), 27–42. https://doi.org/10.1177/2515245917745629