Recently I have been writing about Judea Pearl’s The Book of Why, in which Pearl asks if our reliance on statistics and our adherence to the idea correlation is not causation has gone too far in science. For most people, especially students getting into science and those who have studied politics, reiterating the idea that correlation does not imply causation is important. There are plenty of ways to misinterpret data and there is no shortage of individuals and interest groups who would love to have an official scientist improperly assign causation to a correlation for their own political and economic gain. However, Pearl uses the cigarette wars to show us that failing to acknowledge that correlations can imply causation can also be dangerous.
“The cigarette wars were science’s first confrontation with organized denialism, and no one was prepared,” writes Pearl. For decades there was ample evidence from different fields and different approaches linking cigarette smoking to cancer. However, it isn’t the case that every single person who smokes a cigarette gets cancer. We all know people who smoked for 30 years, and seem to have great lungs. Sadly, we also all know people who developed lung cancer but never smoked. The causation between cigarettes is not a perfect 1:1 correlation with lung cancer, and tobacco companies jumped on this fact.
For years, it was abundantly clear that smoking greatly increased the risk of lung cancer, but no one was willing to say that smoking caused lung cancer, because powerful interest groups aligned against the idea and conditioned policy-makers and the public to believe that in the case of smoking and lung cancer, correlation was not causation. The evidence was obvious, but built on statistical information and the organized denial was stronger. Who was to say if people more susceptible to lung cancer were also more susceptible to start smoking in the first place? Arguments such as these hindered people’s willingness to adopt the clear causal picture that cigarettes caused cancer. People hid behind a possibility that the overwhelming evidence was wrong.
Today we are in a similar situation with climate change and other issues. It is clear that statistics cannot give us a 100% certain answer to a causal question, and it is true that correlation is not necessarily a sign of causation, but at a certain point we have to accept when the evidence is overwhelming. We have to accept when causal models that are not 100% proven have overwhelming support. We have to be able to make decisions without being derailed by organized denialism that seizes on the fact that correlation does not imply causation, just to create doubt and confusion. Pearl’s warning is that failing to be better with how we think about and understand causality can have real consequences (lung cancer in the cigarette wars, and devastating climate impacts today), and that we should take those consequences seriously when we look at the statistics and data that helps us understand our world.
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