by What are RCTs good for? RCTs establish causal claims. They are very good at this. Indeed, given the probabilistic theory of causality it follows formally that positive results in an ideal RCT with treatment C and outcome E deductively implies ‘C causes E in the experimental population’. Though the move from the RCT to a policy prediction that C will cause E when implemented in a new population often goes under the single label, the external validity of the RCT result, this label hides a host of assumptions that we can begin to be far clearer and more explicit about … My overall point, whether one uses the probabilistic theory or some other, is that securing the internal validity of the RCT is not enough. That goes only a very short way indeed towards
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Lars Pålsson Syll considers the following as important: Statistics & Econometrics
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What are RCTs good for?
RCTs establish causal claims. They are very good at this. Indeed, given the probabilistic theory of causality it follows formally that positive results in an ideal RCT with treatment C and outcome E deductively implies ‘C causes E in the experimental population’. Though the move from the RCT to a policy prediction that C will cause E when implemented in a new population often goes under the single label, the external validity of the RCT result, this label hides a host of assumptions that we can begin to be far clearer and more explicit about …
My overall point, whether one uses the probabilistic theory or some other, is that securing the internal validity of the RCT is not enough. That goes only a very short way indeed towards predicting what the cause studied in the RCT will do when implemented in a different population. Of course all advocates of RCTs recognize that internal validity is not external validity. But the gap is far bigger than most let on.
‘Ideally’ controlled experiments tell us with certainty what causes what effects — but only given the right ‘closures.’ Making appropriate extrapolations from (ideal, accidental, natural, or quasi) experiments to different settings, populations, or target systems, is not easy. Causes deduced in an experimental setting still have to show that they come with an export warrant to the target population. The causal background assumptions made have to be justified, and without licenses to export, the value of ‘rigorous’ and ‘precise’ methods — and ‘on-average-knowledge’ — is despairingly small.
The almost religious belief with which its propagators portray it, cannot hide the fact that RCTs cannot be taken for granted to give generalizable results. That something works somewhere is no warranty for us to believe it to work for us here or that it works generally.
The new RCT idolatry is dangerous. Believing randomization is the only way to achieve scientific validity blinds people to searching for and using other methods that in many contexts are better. Insisting on using only one tool often means using the wrong tool.
Another significant and major problem is that researchers who use randomization-based research strategies often set up problem formulations that are not at all the ones we really want answers to, in order to achieve ‘exact’ and ‘rigorous’ results. Design becomes the main thing, and as long as one can get more or less clever experiments in place, they believe they can draw far-reaching conclusions about both causality and the ability to generalize experimental outcomes to larger populations. Unfortunately, this often means that this type of research has a negative bias away from interesting and important problems towards prioritizing method selection. Design and research planning are important, but the credibility of research ultimately lies in being able to provide answers to relevant questions that both citizens and researchers want answers to.
Randomization is not a panacea. It is not the best method for all questions and circumstances. Proponents of randomization make claims about its ability to deliver causal knowledge that is simply wrong. There are good reasons to be sceptical of the now popular — and ill-informed — view that randomization is the only valid and the best method on the market. It is not.