Causal discovery algorithms: A practical guide

Daniel Malinsky; David Danks

doi:10.1111/phc3.12470

journal article Nov 23, 2017

Causal discovery algorithms: A practical guide

Daniel Malinsky David Danks

Philosophy Compass Vol. 13 No. 1 · Wiley

View at Publisher Save 10.1111/phc3.12470

Abstract

Abstract
Many investigations into the world, including philosophical ones, aim to discover causal knowledge, and many experimental methods have been developed to assist in causal discovery. More recently, algorithms have emerged that can also learn causal structure from purely or mostly observational data, as well as experimental data. These methods have started to be applied in various philosophical contexts, such as debates about our concepts of free will and determinism. This paper provides a “user's guide” to these methods, though not in the sense of specifying exact button presses in a software package. Instead, we explain the larger “pipeline” within which these methods are used and discuss key steps in moving from initial research idea to validated causal structure.

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