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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Quantum Non-Markovian Processes Break Conditional Past-Future Independence. [PDF]
Physical Review Letters, 2018 For classical Markovian stochastic systems, past and future events become statistically independent when conditioned to a given state at the present time.
A. A. Budini
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Extended Conditional Independence and Applications in Causal Inference [PDF]
, 2015 The goal of this paper is to integrate the notions of stochastic conditional independence and variation conditional independence under a more general notion of extended conditional independence.
Panayiota Constantinou, A. Dawid
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Conditional tail independence in Archimedean copula models [PDF]
Journal of Applied Probability, 2019 Consider a random vector $\textbf{U}$ whose distribution function coincides in its upper tail with that of an Archimedean copula. We report the fact that the conditional distribution of $\textbf{U}$ , conditional on one of its components, has under a ...
M. Falk, S. Padoan, Florian Wisheckel
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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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A note on the relationship between conditional and unconditional independence and its extensions for Markov kernels [PDF]
Statistica neerlandica (Print), 2017 Two known results on the relationship between conditional and unconditional independence are obtained as a consequence of the main result of this paper, a theorem that uses independence of Markov kernels to obtain a minimal condition, which, added to ...
A. Nogales, P. P'erez
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Graphical modelling of multivariate time series [PDF]
, 2011 We introduce graphical time series models for the analysis of dynamic relationships among variables in multivariate time series. The modelling approach is based on the notion of strong Granger causality and can be applied to time series with non-linear ...
Eichler, Michael
core +6 more sources
Bayesian Estimation of Conditional Independence Graphs Improves Functional Connectivity Estimates. [PDF]
PLoS Computational Biology, 2015 Functional connectivity concerns the correlated activity between neuronal populations in spatially segregated regions of the brain, which may be studied using functional magnetic resonance imaging (fMRI).
Max Hinne +3 more
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Extended de Finetti theorems for boolean independence and monotone independence [PDF]
, 2015 We construct several new spaces of quantum sequences and their quantum families of maps in sense of So{\l}tan. Then, we introduce noncommutative distributional symmetries associated with these quantum maps and study simple relations between them. We will
Weihua Liu
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Conditional exact law of large numbers and asymmetric information economies with aggregate uncertainty [PDF]
Economic Theory, 2014 A stochastic model with a continuum of economic agents often involves shocks at both macro and micro levels. This can be formalized by a continuum of conditionally independent random variables given the macro level shocks.
Lei Qiao, Yeneng Sun, Zhixiang Zhang
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