{"slug": "demystifying-statistical-paradoxes-using-causal-inference", "title": "Demystifying Statistical Paradoxes using Causal Inference", "summary": "Causal inference can demystify statistical paradoxes such as Simpson's paradox, which occurs when a correlation reverses upon splitting data by a third variable, as illustrated by the Berkeley dataset of 12,763 applicants to UC-Berkeley's graduate programs.", "body_md": "Member-only story\n\n# Demystifying Statistical Paradoxes using Causal Inference\n\n## How can causal inference explain statistical paradoxes?\n\nCausal 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.\n\nCausal 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.\n\n**Simpson’s paradox**\n\nSimpson’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…", "url": "https://wpnews.pro/news/demystifying-statistical-paradoxes-using-causal-inference", "canonical_source": "https://pub.towardsai.net/demystifying-statistical-paradoxes-using-causal-inference-4b3cfb4267db?source=rss----98111c9905da---4", "published_at": "2026-07-31 13:01:04+00:00", "updated_at": "2026-07-31 13:13:02.868396+00:00", "lang": "en", "topics": ["machine-learning"], "entities": ["UC-Berkeley"], "alternates": {"html": "https://wpnews.pro/news/demystifying-statistical-paradoxes-using-causal-inference", "markdown": "https://wpnews.pro/news/demystifying-statistical-paradoxes-using-causal-inference.md", "text": "https://wpnews.pro/news/demystifying-statistical-paradoxes-using-causal-inference.txt", "jsonld": "https://wpnews.pro/news/demystifying-statistical-paradoxes-using-causal-inference.jsonld"}}