Causal analysis is a part of our innate desires and sometimes we act on parts believing as long as all the parts are there the total would be there. This is what Pfizer story is telling us. We have no better way of telling how the total effect is when parts are getting together.
The story of torcetrapib is a tale of mistaken causation. Pfizer was operating on the assumption that raising levels of HDL cholesterol and lowering LDL would lead to a predictable outcome: Improved cardiovascular health. Less arterial plaque. Cleaner pipes. But that didn’t happen.
Such failures occur all the time in the drug industry. (According to one recent analysis, more than 40 percent of drugs fail Phase III clinical trials.)
This assumption—that understanding a system’s constituent parts means we also understand the causes within the system—is not limited to the pharmaceutical industry or even to biology. It defines modern science.
There are two lessons to be learned from these experiments. The first is that our theories about a particular cause and effect are inherently perceptual, infected by all the sensory cheats of vision.
The second lesson is that causal explanations are oversimplifications. This is what makes them useful—they help us grasp the world at a glance.
But here’s the bad news: The reliance on correlations has entered an age of diminishing returns.