Abstract
Current debates surrounding the virtues and shortcomings of randomization are symptomatic of a lack of appreciation of the fact that causation can be inferred by two distinct inference methods, each requiring its own, specific experimental design. There is a non-statistical type of inference associated with controlled experiments in basic biomedical research; and a statistical variety associated with randomized controlled trials in clinical research. I argue that the main difference between the two hinges on the satisfaction of the comparability requirement, which is in turn dictated by the nature of the objects of study, namely homogeneous or heterogeneous populations of biological systems. Among other things, this entails that the objection according to which randomized experiments fail to provide better evidence for causation because randomization cannot guarantee comparability is mistaken.
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Notes
“Determining whether there is a causal relationship between variables, A and B, requires that the variables covary, the presence of one variable preceding the other (e.g., A → B), and ruling out the presence of a third variable, C, which might mitigate the influence of A on B” (Leighton 2010, 622). Confounders are conceptualized as rival (usually causal) explanations of the observed difference in outcome between test and control. Thus, if the test and control systems differ in terms of factors that can impact on the measured outcome, the causal inference is deemed inconclusive since the possibility that something other than the manipulated factor may explain the difference in outcomes cannot be ruled out (Chow 2010).
The validity of extrapolations is also compromised. Heterogeneous populations and individuals drawn from these populations are no longer interchangeable experimental surrogates. If the individuals in a population differ in respect to confounding causal factors, causal claims established by studying individuals may not be representative of causal relationships prevalent in the general population, while causal relationships shown to be predominant in a population may not apply to a particular individual drawn from that population. In practical terms, this means that a treatment working well for a group of tested patients may turn out to have little impact in the general population, while a treatment generally successful in a population may not be effective for a particular patient.
The distinction between statistical (or chance) and causal explanations is discussed in Witteveen (forthcoming).
The example is adapted from Hill (1955, Ch. VIII).
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Baetu, T.M. Causal inference in biomedical research. Biol Philos 35, 43 (2020). https://doi.org/10.1007/s10539-020-09760-4
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DOI: https://doi.org/10.1007/s10539-020-09760-4