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An Overview of the Methodologies of Causal Discovery

Cause-and-effect relationships can be discovered through controlled experimentation, human intuition, and causal discovery methods. We give a technical overview of how Causal AI facilitates all three methods, focussing on algorithmic causal discovery.

 

Until recently, discovering cause-and-effect relationships involved conducting a carefully controlled experiment or else relying on raw human intuition. Technological breakthroughs mean that AI can now help with causal discovery. Causal AI autonomously discovers causes in observational data, while also boosting human intuition and experimentation.

This paper is part of our tech reports series, in which we share some of the most significant concepts in the causality literature. Our tech reports are written primarily for a technical audience. For a less technical introduction to causal discovery, take a look at our blog.