If you followed the blog series on causal inference in ecology or you learned about it later, tthis page contains links to all of the pages in the series. You don’t necessarily need to read them in order, but please read the disclaimers in the introduction to the series before reading anything else.
- Introduction to the series
- Controlled experiments
- Randomization and sample size
- The challenge of falsification
- Setting the stage for the Rubin causal model
- The Rubin causal model (part 1)
- The Rubin causal model (part 2)
- The Rubin causal model in ecology
- Concluding thoughts
I will be thinking about causal inference in the context of observational data and statistics, and the causal inference series will say little or nothing about the problems with P-values, Bayes factors, credible/confidence intervals overlapping 0 (or not), and the like. If you’d like to know the concerns I have about them, here are links to old posts on those issues.