Abstract

Causal assessment is the problem of establishing whether a relation between (variable) X and (variable) Y is causal. This problem, to be sure, is widespread across the sciences. According to the ‘received view’, invariance under intervention provides the crucial test to decide whether X causes Y. This account of invariance has been criticised, among other reasons, because it makes manipulations on the putative causal factors fundamental. Consequently, the account is ill suited to those contexts where manipulations are not performed, for instance the social sciences. In this talk, I explain why stability is an important feature of causal relations (in social contexts and elsewhere). I then discuss how to extend the received account of invariance, in a way that manipulations on the putative causal factors are not methodologically fundamental, and yet invariance remains key for causal assessment.