PhD course at NMBU on Causation in Science

30 May – 10 June 2016, NMBU, limited spaces

Some of the chief goals of science are understanding, explanation, prediction and application in new technologies. Only if the world has some significant degree of constancy in what follows from what can these scientific activities be conducted with any purpose. But what is the source of such predictability and how does it operate? In many ways, this is a question that goes beyond science itself – beyond the data – and inevitably requires a philosophical approach. This course starts from the perspective that causation is the main foundation upon which science is based.

Causal or Accidental Correlation – A Challenge for Science

In a Philosophy Bites episode, John Worrall is interviewed about how trustworthy the experiments on which evidence-based medicine rests. Specifically, he discusses how suitable randomised controlled trials (RCTs) are for establishing causation.

How safe is a condom?

On Friends, Rachel becomes pregnant with Ross in spite of using a condom. It comes as a shock to all when they hear that condoms are only 97 percent effective. Or, as Rachel puts it, condoms only work 97 percent of the time. But what does this really mean?

What RCTs can’t do

Establishing causation is not an easy task and a number of scientific methods have been developed specially for this purpose. Randomised controlled trials (RCTs) are by many, but not all, considered to be the gold standard. This means that RCTs are thought to provide the highest form of evidence of causation, and the results of such studies are frequently used to guide expert advice on what to eat, how to teach, which medical treatment to choose, whether to worry about pesticides, and so on. But can we trust RCTs to tell us the full causal story? Not really.

How to interpret statistical data?

Causation is sometimes treated probabilistically. Rather than committing a 100 percent to the effect in our causal predictions, we might say that there is a certain chance of the effect occurring, given the cause. But what do we mean by this? It depends. First, it depends on what we take causation to be. Second, it depends on what we take probability to be.