Proving a cause and effect relationship isn’t easy. Causality is complex, and there are thousands of philosophical and mathematical texts on it that are beyond my understanding. However, I do understand enough to discuss how we arrive at cause and effect relationships in the sciences.
One of the first things drilled into students in a research methods course is that correlation does not equal causation. It’s also taught in a cult-like way that one of the best ways to prove causality in the health and social sciences is with a perfectly designed randomized controlled trial (RCT). Why?
A few reasons:
you can compare what you’re testing to a control, to see if it’s better or worseand if the effects can be ruled out by the placebo or nocebo effect, or through phenomena like regression towards the mean
and you have the sequence of events mapped out (addressing temporal problems, so you know which variable came first and caused the other)
These are difficult to do with observational/epidemiological studies. There are always some spurious links that have nothing to do with the phenomena of interest as seen below:
So perfect RCTs are believed to be one of the best ways to prove causality, and at some point in history, we determined that smoking causes lung cancer in humans. Does that mean we recruited participants, randomized them to a group, and made them smoke?
Nope. Although several unethical experiments on humans have been carried out in the past, this was not one of them. Institutional Review Boards wouldn’t allow an investigation like that, where participants could potentially be harmed to such a large degree.
Wait, if there are no RCTs testing the effects of cigarettes, then how did we determine that smoking causes lung cancer? In the mid-1900s, two researchers (Richard Doll and Austin Bradford Hill) produced data showing that smoking frequency was strongly correlated with lung cancer incidence. The data were compelling. Look at the numbers below.
But again, these were associations from observational studies (case controls), not experiments. What if people who had lung cancer also had genes that made them more likely to smoke? What if they happened to be drawn to smoking? You couldn’t dismiss these possibilities with correlations.
This is what legendary statistician, empiricist, and potato scientist, Ronald A. Fisher, exclaimed at the time.
Fisher was often a critic of these findings and of the surgeon general’s report, that smoking causes lung cancer. He claimed that these conclusions were purely correlational and it’s also what tobacco companies exclaimed to defend their highly profitable product. And they weren’t wrong.
Alright, we’re in a dilemma. Perfectly designed randomized trials are the one of best ways to establish causality, but you can’t conduct randomized experiments to see whether smoking causes cancer, because it’s unethical, so you’re limited to epidemiological/observational studies.
So researchers have to work with what they have. And that’s what they did in the mid-1900s. There were already suspicions at the time, that consumption of tobacco products caused harmful gene mutations from studies carried out in animal models. Specific compounds found in tobacco products had also been established as carcinogens decades before the surgeon general’s report, and researchers were already open to the idea that smoking wasn’t the greatest habit. Unfortunately, rigorous human data were missing.
The results from the British Doctor’s Study (the study conducted by Richard Doll and Austin Bradford Hill) was the nail in the coffin because it provided evidence (although correlational) that smoking frequency increased the incidence rates of lung cancer.
One of the researchers on that original paper (Austin Bradford Hill) went on to establish the Bradford Hill Criteria, a set of viewpoints that Hill had discussed in a speech that he believed helped establish the link between smoking and lung cancer.
Interestingly enough, he was also a pioneer of the randomized controlled trial!
In Hill’s speech, he exclaimed that one could potentially arrive at causality from nine viewpoints:
The strength of the association
The relationship between smoking and lung cancer fit many of these scenarios. There was a biological mechanism (DNA damage from carcinogens in tobacco products), the effect sizes of the associations were large, there was a dose-dependent relationship (groups that smoked more had a higher incidence of lung cancer), there was consistency amongst the data observed, and there was coherence ( multiple lines of evidence to support a relationship: animal models, cellular biology, molecular, and epidemiological, etc.). Robust research needs multiple lines of evidence.
Even without experiments, the evidence from multiple lines was overwhelming.
It’s still worth mentioning that epidemiology has its limits. Temporality is often a problem because it’s hard to figure out which variable caused the other, and not all confounding variables can be adjusted for (over adjustment bias can be a problem and there will always be residual confounding), but if there are multiple lines of evidence to suggest a relationship, then there could be one.