Mediating between causes and probabilities: the use of graphical models in econometrics
Alessio Moneta · Max Planck Institute for Plasma Physics · 2007
The development of macro-econometrics has been persistently fraught with a tension between “deductivist” and “inductivist” approaches to causal inference. The former conceives causes as something that economic theory must provide and that statistical methods must measure. The latter opens the possibility of inferring causes from statistical properties of the data alone. I argue that these conceptions can be interpreted as two opposite responses to the problem of under-determination of theoretical causal relations by statistical properties (the problem of identification). Econometrics offers a clear example as to how the general problem of causal inference can be solved only by delicately mediating between background knowledge and the statistical properties of the data. I show how graphical causal models, appropriately interpreted, can serve this purpose.