Causal inference
Channel: Mikko Rönkkö
Date: 2019-07-10

In a typical quantitative study, we observe a correlation or another statistical association and want to make a causal claim. We do so because causal claims have policy implications such as "if you want your company to be profitable, name a woman as a CEO." This is also important because even if we do not provide any causal interpretations of a research study, the readers of the research might nevertheless interpret correlational results as causal. To make a causal claim, three conditions must be satisfied: 

1) Association - the cause and effect must be statistically associated. 
2) The direction of influence - the cause must precede the effect in our research design.
3) Elimination of rival explanations - we must consider all relevant other causes that might produce the association and rule them out. 

Eliminating rival explanations is the hard part. Randomized experiments and statistical controls are the two most common strategies for eliminating rival explanations. 

Link to slides: https://osf.io/ejyrh