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  1. Karl Pearson and the Logic of Science: Renouncing Causal Understanding (the Bride) and Inverted Spinozism.Julio Michael Stern - 2018 - South American Journal of Logic 4 (1):219-252.
    Karl Pearson is the leading figure of XX century statistics. He and his co-workers crafted the core of the theory, methods and language of frequentist or classical statistics – the prevalent inductive logic of contemporary science. However, before working in statistics, K. Pearson had other interests in life, namely, in this order, philosophy, physics, and biological heredity. Key concepts of his philosophical and epistemological system of anti-Spinozism (a form of transcendental idealism) are carried over to his subsequent works on the (...)
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  • Causality and causal modelling in the social sciences.Federica Russo - 2009 - Springer, Dordrecht.
    The anti-causal prophecies of last century have been disproved. Causality is neither a ‘relic of a bygone’ nor ‘another fetish of modern science’; it still occupies a large part of the current debate in philosophy and the sciences. This investigation into causal modelling presents the rationale of causality, i.e. the notion that guides causal reasoning in causal modelling. It is argued that causal models are regimented by a rationale of variation, nor of regularity neither invariance, thus breaking down the dominant (...)
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  • Factor analysis, information-transforming instruments, and objectivity: A reply and discussion.Stanley A. Mulaik - 1991 - British Journal for the Philosophy of Science 42 (1):87-100.
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  • Toward a synthesis of deterministic and probabilistic formulations of causal relations by the functional relation concept.Stanley A. Mulaik - 1986 - Philosophy of Science 53 (3):313-332.
    There have been two principal paradigms for the formulation of the causal relation--logical implication and functional relationship. In this paper, I present a case for preferring the functional relationship formulation and then discuss how the functional relationship formulation may be implemented in the probabilistic case in a manner analogous to the way others have implemented the logical implication formulation in the probabilistic case. I show how the "local independence" assumption found in many models used in the behavioral and social sciences (...)
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