Results 1 to 10 of about 152 (65)
Decision-theoretic foundations for statistical causality
Journal of Causal Inference, 2021 We develop a mathematical and interpretative foundation for the enterprise of decision-theoretic (DT) statistical causality, which is a straightforward way of representing and addressing causal questions.
Dawid Philip
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The polynomial learning with errors problem and the smearing condition
Journal of Mathematical Cryptology, 2022 As quantum computing advances rapidly, guaranteeing the security of cryptographic protocols resistant to quantum attacks is paramount. Some leading candidate cryptosystems use the learning with errors (LWE) problem, attractive for its simplicity and ...
Babinkostova Liljana +4 more
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Causality and independence in perfectly adapted dynamical systems
Journal of Causal Inference, 2023 Perfect adaptation in a dynamical system is the phenomenon that one or more variables have an initial transient response to a persistent change in an external stimulus but revert to their original value as the system converges to equilibrium.
Blom Tineke, Mooij Joris M.
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Potential outcome and decision theoretic foundations for statistical causality
Journal of Causal Inference, 2023 In a recent work published in this journal, Philip Dawid has described a graphical causal model based on decision diagrams. This article describes how single-world intervention graphs (SWIGs) relate to these diagrams.
Richardson Thomas S., Robins James M.
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Decision-theoretic foundations for statistical causality: Response to Pearl
Journal of Causal Inference, 2022 I thank Judea Pearl for his discussion of my paper and respond to the points he raises. In particular, his attachment to unaugmented directed acyclic graphs has led to a misapprehension of my own proposals. I also discuss the possibilities for developing
Dawid Philip
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Journal of Causal Inference, 2021 A result from a standard linear model course is that the variance of the ordinary least squares (OLS) coefficient of a variable will never decrease when including additional covariates into the regression. The variance inflation factor (VIF) measures the
Ding Peng
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Decision-theoretic foundations for statistical causality: Response to Shpitser
Journal of Causal Inference, 2022 I thank Ilya Shpitser for his comments on my article, and discuss the use of models with restricted interventions.
Dawid Philip
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Causation and decision: On Dawid’s “Decision theoretic foundation of statistical causality”
Journal of Causal Inference, 2022 In a recent issue of this journal, Philip Dawid (2021) proposes a framework for causal inference that is based on statistical decision theory and that is, in many aspects, compatible with the familiar framework of causal graphs (e.g., Directed Acyclic ...
Pearl Judea
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Likelihood decision functions [PDF]
, 2012 In both classical and Bayesian approaches, statistical inference is unified and generalized by the corresponding decision theory. This is not the case for the likelihood approach to statistical inference, in spite of the manifest success of the ...
Cattaneo, Marco E. G. V.
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Quadratic distances on probabilities: A unified foundation [PDF]
, 2007 This work builds a unified framework for the study of quadratic form distance measures as they are used in assessing the goodness of fit of models.
Chen, Shu-Chuan +4 more
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