Introduction to the foundations of causal discovery

Abstract

This article presents an overview of several known approaches to causal discovery. It is organized by relating the different fundamental assumptions that the methods depend on. The goal is to indicate that for a large variety of different settings the assumptions necessary and sufficient for causal discovery are now well understood.

Studying the effects of intervention programmes on household energy saving behaviours using graphical causal models

“…Causal discovery based on graphical causal models use the notion of conditional independence, and d-separation in particular, to learn the underlying DAG structure. It is beyond the scope of this paper to discuss causal discovery in detail, and interested readers are guided to Eberhardt [26] for a brief introduction, Spirtes and Zhang [27] for a review, and Spirtes et al [15] for a detailed presentation of causal discovery algorithms.…”

Cognitive Identity Management: Risks, Trust and Decisions using Heterogeneous Sources

“…2) Causal analysis of risks and trust is based on the "causeeffect" paradigm. In particular, Granger causality analysis is an advanced tool for this purpose [18], [48].…”

Simulations evaluating resampling methods for causal discovery: ensemble performance and calibration

“…Beyond that, however, they vary considerably. The field is too large to review here, so we will only discuss the algorithm used in this investigation; for recent introductory papers on causal discovery, see [6] or [12].…”