In this paper, we discuss several concepts in causal inference in terms of causal diagrams proposed by Pearl (1993, 1995a, b), and we give conditions for non-confounding, homogeneity and collapsibility for causal e€ects without knowledge of a completely constructed causal diagram. We ®rst introduce the concepts of non-confounding, conditional non-confounding, uniform non-confounding, homogeneit...

New statistical methods allow discovery of causal models from observational data in some circumstances. These models permit both probabilistic inference and causal inference for models of reasonable size. Many domains, such as education, can benefit from such methods. Educational research does not easily lend itself to experimental investigation. Research in laboratories is artificial and poten...

Causality is a complex concept, which roots its developments across several fields, such as statistics, economics, epidemiology, computer science, and philosophy. In recent years, the study of causal relationships has become crucial part Artificial Intelligence community, causality can be key tool for overcoming some limitations correlation-based Machine Learning systems. research generally div...

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We give an up-to-date perspective with a general overview of the theory of causal properties, the derived causal structures, their classification and applications, and the definition and construction of causal boundaries and of causal symmetries, mostly for Lorentzian manifolds but also in more abstract settings. PACS numbers: 04.20.Gz, 04.20.Cv, 02.40.Ky