To what use can causal claims established in good studies be put? We give examples of studies from which inaccurate inferences were made about target policy situations. The usual diagnosis is that the studies in question lack external validity, which means that the same results do not hold in the target as in study. That’s a label that just repeats what we already knew. We offer a deeper analysis. Our analysis points to the need for interdisciplinarity and to the demand (...) to focus not on the study – as the expression ‘external validity’ invites you to do – but on the target. The call for interdisciplinary approaches to real life problems is common since it is widely acknowledged that what happens in the real world seldom falls under the auspices of any single research domain. Our focus is on one specific real life problem: how to use causal claims from good studies to help predict whether the policies tested will work in a new situation. Our analysis of what it takes to back up these predictions points up very specific stages in the process of prediction where we are bound to get it wrong if we do not diversify our concepts, our knowledge and our methods. We isolate two reasons inferences from study to target fail. First, policy variables do not produce results on their own; they need helping factors. The distribution of helping factors is likely to be unique or local for each study, so one cannot expect external validity to be all that common. Second, researchers often give too concrete a description of the cause in the study for it to carry over to the target.ion is necessary to get causes that travel. There is no sure-fire way to guard against these problems. But the unavailability of one perfect tool does not imply there are no second best contrivances. Two general pointers for Good Practice in policy advice follow from our diagnosis: focus on the concrete details in the target and use cross discipline heuristics that diversify background knowledge. (shrink)

To what use can causal claims established in good policy studies be put? We isolate two reasons inferences from study to target fail. First, policy variables do not produce results on their own; they need helping factors. The distribution of helping factors is likely to be unique or local for each study, so one cannot expect external validity to be all that common. Second, researchers often give too concrete a description of the cause in the study for it to carry (...) over to the target. Abstraction is necessary to get causes that travel. There is no sure-fire way to guard against these problems. But the unavailability of one perfect tool does not imply there are no second best contrivances. Two general pointers for Good Practice in policy advice follow from our diagnosis: focus on the concrete details in the target and use cross discipline heuristics that diversify background knowledge.¿Qué uso podemos hacer de las tesis causales que encontramos en los buenos estudios sobre política aplicada? Distinguimos dos razones por las que pueden fallar las inferencias desde la población en el estudio a la población general. En primer lugar, las variables que usamos en política no generan resultados por sí solas. Necesitan factores coadyuvantes. La distribución de estos factores es probablemente única o local en cada estudio, así que no hay motivos para esperar su validez externa. En segundo lugar, los investigadores a menudo dan descripciones demasiado concretas de la causalidad en el estudio como para poder generalizarlas. La abstracción es necesaria para obtener causas que viajen. No hay ningún modo absolutamente seguro de evitar semejantes fallos. Pero esto no implica que no haya arreglos subóptimos. De nuestro diagnóstico se siguen dos orientaciones generales sobre las buenas prácticas en la asesoría política: concentrémonos en los detalles concretos de la población en la que pretendemos intervenir y usemos heurísticas transdisciplinares que diversifiquen nuestro conocimiento de fondo. (shrink)

To what use can causal claims established in good studies be put? We give examples of studies from which inaccurate inferences were made about target policy situations. The usual diagnosis is that the studies in question lack external validity, which means that the same results do not hold in the target as in study. That’s a label that just repeats what we already knew. We offer a deeper analysis. Our analysis points to the need for interdisciplinarity and to the demand (...) to focus not on the study – as the expression ‘external validity’ invites you to do – but on the target. The call for interdisciplinary approaches to real life problems is common since it is widely acknowledged that what happens in the real world seldom falls under the auspices of any single research domain. Our focus is on one specific real life problem: how to use causal claims from good studies to help predict whether the policies tested will work in a new situation. Our analysis of what it takes to back up these predictions points up very specific stages in the process of prediction where we are bound to get it wrong if we do not diversify our concepts, our knowledge and our methods. We isolate two reasons inferences from study to target fail. First, policy variables do not produce results on their own; they need helping factors. The distribution of helping factors is likely to be unique or local for each study, so one cannot expect external validity to be all that common. Second, researchers often give too concrete a description of the cause in the study for it to carry over to the target.ion is necessary to get causes that travel. There is no sure-fire way to guard against these problems. But the unavailability of one perfect tool does not imply there are no second best contrivances. Two general pointers for Good Practice in policy advice follow from our diagnosis: focus on the concrete details in the target and use cross discipline heuristics that diversify background knowledge. (shrink)

Julian Reiss finds an insoluble paradox in the claims that economic models are at the same time false, nevertheless explanatory, and that only true explanations explain. But the claim that they are false is itself false. A closer look at what ?truth? may mean is needed.

In een tijd waarin 'nepnieuws' zich steeds nadrukkelijker naar de voorgrond dringt, is de vraag naar wat waarheid is buitengewoon belangrijk. Ook vragen of wetenschappelijke studies betrouwbaar zijn en of een (sociale) wetenschapper neutraal beleidsadvies kan geven zijn aan de orde van de dag. Daarop kan de wetenschapsfilosofie antwoord geven. Dit boek biedt kritische reflectie op belangrijke vraagstukken in de filosofie van de sociale wetenschappen. De sociale wetenschapper wordt ingeleid in wetenschapsfilosofische thema's als causaliteit, verklaringsstijlen, reductionisme en holisme, statistiek, modellen (...) en onderzoeksparadigma's. Daarnaast is er ruim aandacht voor de wetenschappelijke praktijk en de positie die (sociale) wetenschappers innemen in het maatschappelijk debat. Veel wetenschappers zijn immers geneigd liever neutraal te blijven en zich niet in een maatschappelijk debat te mengen. Maar is dat eigenlijk wel wenselijk en mogelijk?"--Page 4 of cover. (shrink)

Volume 27, Issue 1, March 2020, Page 89-92.

Stipulating universal propositions with a ceteris paribus clause is normal practice in science and especially in economics. Yet there are several problems associated with the use of ceteris paribus clauses in theorising and in policy matters. This paper first investigates three questions: how can ceteris paribus clauses be non-vacuous? How can ceteris paribus laws be true? And how can they help in formulating successful policy interventions in a diversity of contexts? It turns out that ceteris paribus clauses are not always (...) used legitimately. They are meant to fence off a theory from disturbing factors, but economists who do not specify the clause well enough tend to fence variables in rather than off. In such cases, it would be better to use theoretical abstraction, which is something very different from the use of ceteris paribus clauses. However, abstract theorising conceptually leads one away from the concrete detail of real world situations in which policies take place. Hence, a fourth question arises: how can policy interventions be properly designed on the basis of abstract laws? To answer this question, I defend interdisciplinarity in concept choice. (shrink)