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.