In this blog post I will describe eight basic rules that govern the relationship between causal mechanisms in the real world and associations/correlations we can observe in data. To make each rule as easy as possible to understand, I will describe each rule both in words and in causal graph and logic terms, and I will offer some very simple simulation R code for each rule to demonstrate how it works in practice.
In this blog post I will explain why it makes sense to think about causal explanations when we see correlation, why correlation does not always imply causation, and which alternative causal models to consider when you are trying to figure out why two variables in your data are correlated. The blog post is intended as a non-technical introduction. I use only words and pictures to explain all concepts and logic, and I use a hypothetical example from health psychology to illustrate. The post should be relevant to anyone who is interested in using data to understand causal mechanisms.
A method for calculating the number of alternative causal models, given a number of variables/nodes.