by RCTs — the illusion of assumption-free learning Blinding is rarely possible in economics or social science trials, and this is one of the major differences from most (although not all) RCTs in medicine, where blinding is standard, both for those receiving the treatment and those administering it … Subjects in social RCTs usually know whether they are receiving the treatment or not and so can react to their assignment in ways that can affect the outcome other than through the operation of the treatment; in econometric language, this is akin to a violation of exclusion restrictions, or a failure of exogeneity … Note also that knowledge of their assignment may cause people to want to cross over from treatment to control, or vice versa, to drop out of the program, or to change their behavior in the trial depending on their assignment. In extreme cases, only those members of the trial sample who expect to benefit from the treatment will accept treatment. Consider, for example, a trial in which children are randomly allocated to two schools that teach in different languages, Russian or English, as happened during the breakup of the former Yugoslavia.
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RCTs — the illusion of assumption-free learning
Blinding is rarely possible in economics or social science trials, and this is one of the major differences from most (although not all) RCTs in medicine, where blinding is standard, both for those receiving the treatment and those administering it … Subjects in social RCTs usually know whether they are receiving the treatment or not and so can react to their assignment in ways that can affect the outcome other than through the operation of the treatment; in econometric language, this is akin to a violation of exclusion restrictions, or a failure of exogeneity …
Note also that knowledge of their assignment may cause people to want to cross over from treatment to control, or vice versa, to drop out of the program, or to change their behavior in the trial depending on their assignment. In extreme cases, only those members of the trial sample who expect to benefit from the treatment will accept treatment. Consider, for example, a trial in which children are randomly allocated to two schools that teach in different languages, Russian or English, as happened during the breakup of the former Yugoslavia. The children (and their parents) know their allocation, and the more educated, wealthier, and less-ideologically committed parents whose children are assigned to the Russian-medium schools can (and did) remove their children to private English-medium schools. In a comparison of those who accepted their assignments, the effects of the language of instruction will be distorted in favor of the English schools by differences in family characteristics. This is a case where, even if the random number generator is fully functional, a later balance test will show systematic differences in observable background characteristics between the treatment and control groups; even if the balance test is passed, there may still be selection on unobservables for which we cannot test …
Various statistical corrections are available for a few of the selection problems non- blinding presents, but all rely on the kind of assumptions that, while common in observational studies, RCTs are designed to avoid. Our own view is that assumptions and the use of prior knowledge are what we need to make progress in any kind of analysis, including RCTs whose promise of assumption-free learning is always likely to be illusory …
This only confirms that ‘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. “It works there” is no evidence for “it will work here”. Causes deduced in an experimental setting still have to show that they come with an export-warrant to the target system. The causal background assumptions made have to be justified, and without licenses to export, the value of ‘rigorous’ methods and ‘on-average-knowledge’ is despairingly small.