Science [ sahy-uhns ] (noun):
the act of trying to figure out what the world is really like
Written by Peder M. Isager
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 propose a four one-sided test of significance (FOST) that can be used to test whether an effect is smaller than OR larger than OR equivalent to the smallest effect size of interest. I lay out the general setup and interpretation of FOST, and ask readers to help me evaluate its validity.
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.
Given that most research is original and we have limited resources available for replication, we need guidelines for study selection in replication research. But what makes a study worth replicating? In our unconference session at SIPS this year, we had the pleasure of discussing this problem with fifty-some enthusiastic scholars. Here is a quick summary of what they taught us. First off, we went into this session with our own prior beliefs about what makes a study worth replicating.
A method for calculating the number of alternative causal models, given a number of variables/nodes.