Abstract According to the theory developed here, we may trace out processes emanating from a cause in such way that any consequence lying along one of these counts as an effect cause. This gives intuitive verdicts diverse range problem cases literature. Its claims about causation will never be retracted when include additional variables our model. And it validates some plausible principles caus...

nowadays we know effective supply chain management a key to business success. therefore, supply chain managers use many practices for scm effectiveness and many tools as their enablers. nevertheless, literature about causal relations between sc enablers and scm practices and performance is scarce. this study reports a cognitive mapping of causal relationships between scm practices, sc enablers ...

This paper includes a causal-based modelling of software process models in order to analyse the correct relationships between the different (key) process areas of these models. A first short description of causal network approaches shows the identified problems and possible benefits using these formal techniques in the software engineering area. The definition and extension of the causal modell...

An important goal in both transfer learning and causal inference is to make accurate predictions when the distribution of the test set and the training set(s) differ. Such a distribution shift may happen as a result of an external intervention on the data generating process, causing certain aspects of the distribution to change, and others to remain invariant. We consider a class of causal tran...

In recent papers, Lee & Holyoak (2007, 2008a, 2008b) argue that extant models of analogy fail to explain how people draw inferences from causal analogies. In the current research, we argue that structure-mapping theory sufficiently explains the analogical inferences drawn from these causal analogies, and that, contrary to L&H‘s claims, the effect inference can indeed be evaluated by a post-anal...

This paper treats the calculation of the effect of an intervention (also called causal effect) on a variable from a combination of observational data and some theoretical assumptions. Observational data implies that the modeler has no way to do experiments to assess the effect of one variable on some others, instead he possesses data collected by observing variables in the domain he is investig...