Correlation, causation, and confusion

By Nick Barrowman via thenewatlantis.com   Article

“Causation has long been something of a mystery, bedeviling philosophers and scientists down through the ages. What exactly is it? How can it be measured — that is, can we assess the strength of the relationship between a cause and its effect? What does an observed association between factors — a correlation — tell us about a possible causal relationship? How do multiple factors or causes jointly influence outcomes? And does causation even exist “in the world,” as it were, or is it merely a habit of our minds, a connection we draw between two events we have observed in succession many times, as Hume famously argued? The rich philosophical literature on causation is a testament to the struggle of thinkers throughout history to develop satisfactory answers to these questions. Likewise, scientists have long wrestled with problems of causation in the face of numerous practical and theoretical impediments. …

In recent years, it has become widely accepted in a host of diverse fields, such as business management, economics, education, and medicine, that decisions should be “evidence-based” — that knowledge of outcomes, gathered from scientific studies and other empirical sources, should inform our choices, and we expect that these choices will cause the desired results. We invest large sums in studies, hoping to find causal links between events. Consequently, statistics have become increasingly important, as they give insight into the relationships between factors in a given analysis. However, the industry of science journalism tends to distort what studies and statistics show us, often exaggerating causal links and overlooking important nuances.

Causation is rarely as simple as we tend to assume and, perhaps for this reason, its complexities are often glossed over or even ignored. This is no trifling matter. Misunderstanding causal links can result in ineffective actions being chosen, harmful practices perpetuated, and beneficial alternatives overlooked. Unfortunately, the recent hype about ‘big data’ has encouraged fanciful notions that such problems can be erased thanks to colossal computing power and enormous databases. The presumption is that sheer volume of information, with the help of data-analysis tools, will reveal correlations so strong that questions about causation need no longer concern us. If two events occur together often enough, so the thinking goes, we may assume they are in fact causally linked, even if we don’t know how or why.”

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