Many researchers appear to operate under the impression that causal models lead to more accurate forecasts than those provided by naive models (or “projections”). This study was based on the premise that causal models lead to better forecasts than do naive models in certain situations. The key element of these situations is that there are “large changes.” One situation where large changes might...

Several paradigms exist for modeling causal graphical models for discrete variables that can handle latent variables without explicitly modeling them quantitatively. Applying them to a problem domain consists of different steps: structure learning, parameter learning and using them for probabilistic or causal inference. We discuss two well-known formalisms, namely semi-Markovian causal models a...

In this paper, we describe a dynamic causal model (DCM) of steady-state responses in electrophysiological data that are summarised in terms of their cross-spectral density. These spectral data-features are generated by a biologically plausible, neural-mass model of coupled electromagnetic sources; where each source comprises three sub-populations. Under linearity and stationarity assumptions, t...

Dynamic Causal Modelling (DCM) and the theory of autopoietic systems are two important conceptual frameworks. In this review, we suggest that they can be combined to answer important questions about self-organising systems like the brain. DCM has been developed recently by the neuroimaging community to explain, using biophysical models, the non-invasive brain imaging data are caused by neural p...

The assessment of a probability distribution that is associated with a Bayesian network is a challenging task, even if its topology is sparse. Special probability distributions, based on the notion of causal independence, have therefore been proposed, as these allow defining a probability distribution in terms of Boolean combinations of local distributions. In Bayesian networks which need to mo...

This article explains causal relationships in conceptual models of mental health phenomena. Direct, moderating, mediating, and reciprocal effects among variables are defined, appropriate statistical analyses are described, and the correct interpretations of moderating versus mediating effects are discussed. Examples are provided that will help the reader to distinguish between moderating and me...

Arby is a software for writing expert electronic systems system or higher order language systems to do diagnosis in . As such, it is similar to EMYCIN (van &lle 1962) in application, but quite different in design. It is rule-based to an extent, but the rules are written in predicate calculus. It resembles Caduceus (Pople 1977) in its me&anisms for refining and combining hypotheses.

Abstract. Starting from the observation that Friedman’s partial dependence plot has exactly the same formula as Pearl’s back-door adjustment, we explore the possibility of extracting causal information from black-box models trained by machine learning algorithms. There are three requirements to make causal interpretations: a model with good predictive performance, some domain knowledge in the f...

Functional integration in the brain rests on anatomical connectivity (the presence of axonal connections) and effective connectivity (the causal influences mediated by these connections). The deployment of anatomical connections provides important constraints on effective connectivity, but does not fully determine it, because synaptic connections can be expressed functionally in a dynamic and c...