IV Computational Mechanics 9 A Causal States . . . . . . . . . . . . . . 9 Causal States of a Process Defined . . . 9 1 Morphs . . . . . . . . . . . . . . . . 10 Independence of Past and Future Conditional on a Causal State . . . 10 2 Homogeneity . . . . . . . . . . . . . 11 Strict Homogeneity . . . . . . . . . 11 Weak Homogeneity . . . . . . . . . 11 Strict Homogeneity of Causal States 11 B Ca...

This paper presents a systematic methodology for building causal models that can be used for fault detection and isolation. The aim of a causal model is to capture the influences between the variables of a continuous process and to generate qualitative and quantitative knowledge that is interpreted by a diagnostic module. Following a model-based approach for fault detection, the diagnostic modu...

Causal message ordering is a partial ordering of messages in a distributed computing environment. It places a restriction on communication between processes by requiring that if the transmission of messagemi to process pk necessarily preceded the transmission of message mj to the same process, then the delivery of these messages to that process must be ordered such that mi is delivered before m...

We encode causal space-time structure within categorical process structure, by restricting the tensor to space-like separated entities, i.e. between which there is no causal flow of information. In such a causal category, a privileged set of morphisms captures the idea of an event horizon. This structure enables us to derive statements independent of specific models and detailed descriptions of...

Home attachment is positive emotional bond between home and its dwellers which may result in safety, solace and tranquility for them. To create this kind of relationship some physical and social prerequisites need to exist inside home. This article aims to answer the question "whether house outdoor environment features are also effective in developing home attachment or not?" Aiming to answer t...

When dealing with a dynamic causal system people may employ a variety of different strategies. One of these strategies is causal learning, that is, learning about the causal structure and parameters of the system acted upon. In two experiments we examined whether people spontaneously induce a causal model when learning to control the state of an outcome value in a dynamic causal system. After t...

We consider the problem of learning the functions computing children from parents in a Structural Causal Model once the underlying causal graph has been identified. This is in some sense the second step after causal discovery. Taking a probabilistic approach to estimating these functions, we derive a natural myopic active learning scheme that identifies the intervention which is optimally infor...

In this paper, we apply a recently developed differential approach to inference in staged tree models to causal inference. Staged trees generalise modelling techniques established for Bayesian networks (BN). They have the advantage that they can depict highly nuanced structure impossible to express in a BN and also enable us to perform causal manipulations associated with very general types of ...

Health information technology evaluators need to distinguish between intervention efficacy as assessed in the ideal circumstances of clinical trials and intervention effectiveness as assessed in the real world circumstances of actual practice. Because current evaluation study designs do not routinely allow for this distinction, we have developed a framework for evaluation of implementation fide...

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...