A beginner’s guide to causal inference methods: randomized controlled trials, difference-in-differences, synthetic control, and A/B testing
This article is intended for beginners who want a comprehensive introduction to causality and causal inference methods, explained with minimal math.
When it comes to causality, we simply can’t avoid this classic statement: “Correlation does not imply causation.” And a classic example is that just because ice cream sales and drowning incidents are correlated, one does not cause the other. You’ve probably heard many such examples illustrating the difference between the two. While these examples are often straightforward, the distinction can become blurred in actual analyses.
Without a clear understanding of how causality is measured, it is easy to make incorrect causal inferences. In this regard, one question I often encounter is, “Yes, we know that correlation does not mean causation, but what about a regression analysis?”. The short answer is that linear regression, by default, does not provide any causal statements unless we undertake appropriate steps — this is where causal inference methods come into play.