(Although this article discusses correlation and causation in terms of medical research, it can also be used for the theory of Anthropogenic Global Warming, since we often hear these two words bandied about whenever the topic arises. — DQ)
Nick Barrowman — The New Atlantis (A Journal of Technology & Society)
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.
Yet when speaking of causation, we usually take for granted some notion of what it is and how we are able to assess it. We do this whenever we consider the consequences of our actions or those of others, the effects of government interventions, the impacts of new technologies, the consequences of global warming, the effectiveness of medical treatments, the harms of street drugs, or the influence of popular movies. Some causal statements sound strong, such as when we say that a treatment cured someone or that an announcement by the government caused a riot. Others give a weaker impression, such as when we say that the detention of an opposition leader affected international perceptions. Finally, some statements only hint at causation, such as when we say that the chemical bisphenol A has been linked to diabetes.
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. Continue reading here…..