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How can causal inference explain statistical paradoxes? #
Causal inference is an important tool for data analysis that makes it possible to determine whether a change in one variable truly causes an effect on another, rather than merely being correlated with it. By revealing cause-and-effect relationships, it provides a framework for answering actionable “what if” questions and allows you to design more effective interventions and make decisions based on evidence.
Causal inference is genuinely powerful for demystifying statistical paradoxes that arise when fundamentally different concepts are confused, such as observational patterns versus causal effects, marginal versus conditional distributions, or population-level versus individual-level claims. In this article, we will explore several classic statistical paradoxes and show how causal inference provides clear explanations for each.
Simpson’s paradox
Simpson’s Paradox refers to a statistical phenomenon in which the correlation between two variables disappears or reverses when you split the data by a third variable. Here, we use the Berkeley dataset as a classic example of this paradox. The Berkeley Dataset contains all 12,763 applicants to UC-Berkeley’s graduate programs in…