Causality is the intuitive notion that some events happening “result” in other events happening. What’s going on with that? What does it mean for A to cause B? How do we figure out the causal relationship between things?

When formalized, causal relationships are usually formalized as a directed acyclic graph from parent events to child events saying how to compute the probable child given the state of its parents.

Local introductions

• Causal Diagrams and Causal Models

• Causality: A Brief Introduction

External introductions

• The Book of Why [Amazon]

• An Introduction to Causal Inference

See also

• Free will

• Teleology

• Beliefs require observations

External links

• Philip Dawid’s explication of Pearl’s model, and two ways of thinking about nonrandom sampling by Philip Dawid and Andrew Gelman—Causal inference as “the task of using data collected under one regime to infer about the properties of another”.

• Resolving disputes between J. Pearl and D. Rubin on causal inference and More on Pearl’s and Rubin’s frameworks for causal inference by Andrew Gelman

• If correlation doesn’t imply causation, then what does? by Michael Nielsen

• Correlation is Evidence of Causation by Kim Øyhus

• Judea Pearls’s works: Causality: Models, Reasoning, and Inference, Book of Why, A Primer on Causality