Abstract
A fundamental task in various disciplines of science, including biology, is to find underlying causal relations and make use of them. Causal relations can be seen if interventions are properly applied; however, in many cases they are difficult or even impossible to conduct. It is then necessary to discover causal relations by analyzing statistical properties of purely observational data, which is known as causal discovery or causal structure search. This paper aims to give a introduction to and a brief review of the computational methods for causal discovery that were developed in the past three decades, including constraint-based and score-based methods and those based on functional causal models, supplemented by some illustrations and applications.
Highlights
Almost all of science is about identifying causal relations and the laws or regularities that govern them
This paper is about the scientific application of a kind of representation of causal relations, directed graphical causal models (DGCMs), and computerized methods for finding true causal representations of that kind from data, whether observational or experimental or both. We focus on it here because while apparently first proposed in 2000 for studies of gene expression (Murphy and Mian, 1999; Friedman et al, 2000; Spirtes et al, 2000), the models have found wide use in systems biology, especially in omics and in neural connectivity studies, and there has recently been an explosion in the number of algorithms that have been proposed and applied for discovering such representations in biological applications
Understanding causal relations is helpful for constructing interventions to achieve certain objectives and enables making predictions under interventions
Summary
Almost all of science is about identifying causal relations and the laws or regularities that govern them. Since the seventeenth century beginnings of modern science, there have been two kinds of procedures, and resulting kinds of data, for discovering causes: manipulating and varying features of systems to see what other features do or do not change; and observing the variation of features of systems without manipulation. Both methods shone in the seventeenth century, when they were intertwined as they are today. For whom Torricelli worked, had identified (qualitatively) the orbits of Jovian satellites from observational time series, and characterized sunspots. Modern molecular biology is an experimental subject, but the foundation of biology, in Darwin’s Origin of Species, has only a single experiment, the drifting of seeds
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