Sentence examples for problem of causal from inspiring English sources

Although the Bayesian causal inference model explains the problem of causal inference in cue combination successfully, how causal inference in cue combination could be implemented by neural circuits, is unclear.

The problem of causal inference in observational studies is illustrated in Fig. 1.

This generalized Thompson sampling can be straightforwardly applied to the problem of causal induction.

Holland refers to this as the fundamental problem of causal inference.

This paper provided a review and synthesis of the problem of causal inference in large-scale educational assessments from a Bayesian perspective.

Of course, this is impossible in practice and is referred to as the fundamental problem of causal inference (Holland 1986 p. 947).

She is a professor in the psychology department and works on problems of causal knowledge and learning, intuitive theory formation, and "theory of mind". Her work explores the relation between empirical work in cognitive development and classical philosophical problems in epistemology and philosophy of mind.

One of the main methods to deal with problems of causal inference is the framework of causal graphical models (Pearl [2000]).

A number of researchers in artificial intelligence have recently deployed one version of circumscription (namely, the stable models of Gelfond and Lifschitz [1988]) to problems of causal reasoning, building on an idea of Norman McCain and Hudson Turner's [McCain and Turner 1997].

In Pearl (2000), while he presents the (CMC) as a kind of ideal case where problems of causal identification are always soluble, the methods of causal identification explored in Chapter 3 of that book are developed from 'first principles': deterministic functions with probability distributions on error variables that need not be independent.

The main contribution of the present paper is to show in how far generalized Thompson sampling can be regarded as an optimal solution method for adaptive decision-making in the presence of information-processing constraints and how this framework can be extended to solve problems of causal induction.