This post by Emily Riederer is a great summary of four potential approaches to trying to infer causality from data where a formal experiment wasn’t run.
She covers the concepts behind
- stratification
- propensity score weighting
- regression discontinuity
- difference in differences analysis
in a short and very readable article, with business-focussed examples.