A new method for determining causality gives scientists a more holistic view of the causal role that contributing factors play within just about any system.
Cause and effect. We understand this concept from an early age. Tug on a pull toy's string, and the toy follows. Naturally, things get much more complicated as a system grows, as the number of variables increases, and as noise enters the picture. Eventually, it can become almost impossible to tell whether a variable is causing an effect or is simply correlated or associated with it.
The new mathematical tool can tease out the contributions that each variable in a system makes to a measured effect -- both separately and, more importantly, in combination. The team describes its new method, called synergistic-unique-redundant decomposition of causality , in a paper published today, November 1, in the journalThe new model can be used in any situation in which scientists are trying to determine the true cause or causes of a measured effect.
"Previous methods will only tell you how much causality comes from one variable or another," explains Lozano-Durán."What is unique about our method is its ability to capture the full picture of everything that is causing an effect." Synergistic causality, on the other hand, involves multiple variables that must work together to produce an effect. Each variable on its own will not yield the same outcome. For instance, a patient takes medication A, but he does not recuperate from his illness. Similarly, when he takes medication B, he sees no improvement. But when he takes both medications, he fully recovers. Medications A and B are synergistic.
"Our method will consistently give you a meaningful answer across all these cases," says Gonzalo Arranz, a postdoctoral researcher in the Graduate Aerospace Laboratories at Caltech, who is also an author of the paper."Other methods mix causalities that should not be mixed, and sometimes they get confused. They get a false positive identifying a causality that doesn't exist, for example.
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