Causal manipulation theorems proposed by Spirtes et al. in the context of directed probabilistic graphs, such as Bayesian networks, do not model so called reversible causal mechanisms, i.e., mechanisms that are capable of working in several directions, depending on which of their variables are manipulated exogenously. An example involving reversible causal mechanisms is the power train of a car...

This paper presents the theory and formalism of fuzzy causal probabilistic networks (FCPN) and show their current and potential applications in multisensor data fusion. A fuzzy causal probabilistic network (FCPN) is a directed acyclic graph representing the joint probability distributions of a set of fuzzy random variables describing a problem domain. FCPNs extend causal probabilistic networks ...

We compute Teitelboim’s causal propagator in the context of canonical loop quantum gravity. For the Lorentzian signature, we find that the resultant power series can be expressed as a sum over branched, colored two-surfaces with an intrinsic causal structure. This leads us to define a general structure which we call a “causal spin foam”. We also demonstrate that the causal evolution models for ...

Causal inference is a fundamental component of cognition and perception. Probabilistic theories of causal judgment (most notably causal Bayes networks) derive causal judgments using metrics that integrate contingency information. But human estimates typically diverge from these normative predictions. This is because human causal power judgments are typically strongly influenced by beliefs conce...

We describe abstract (p,q) string networks which are the string networks of Sen without the information about their embedding in a background spacetime. The non-perturbative dynamical formulation invented for spin networks, in terms of causal evolution of dual triangulations, is applied on them. The formal transition amplitudes are sums over discrete causal histories that evolve (p,q) string ne...