Conditionals, Causality and Conditional Probability

Rooij, Robert van; Schulz, Katrin

doi:10.1007/s10849-018-9275-5

Cited by 43 publications

(55 citation statements)

References 41 publications

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“…In addition, conditional probability assessment has been proposed as a complementary test to dependence analysis for the investigation of causality. 37 Accordingly, in order to further test if neighborhood connectivity is a good candidate for removing the positional bias of betweenness centrality and collective influence, we performed conditional probability assessments. As a result, the measurements based on whole networks as well as their split-half random samples determined that betweenness centrality/collective influence and neighborhood connectivity deviate from their corresponding means in opposite directions ( Figures 4 E and 4F).…”

Section: Resultsmentioning

confidence: 99%

“… 57 Also, conditional probability assessment is a complementary test to dependence analysis for proving causality. 37 Accordingly, subsequent to the interrogation of the innate characteristics of selected centrality measures and the nature of their associations, we considered assessing their correlation, dependence, and the conditional probability of their opposite behaviors in order to reach more reliable conclusions. A schematic workflow of the methods implemented for the assessment of innate features and association of selected network centrality measures is shown in Figure 1 .…”

Section: Methodsmentioning

confidence: 99%

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Integrated Value of Influence: An Integrative Method for the Identification of the Most Influential Nodes within Networks

2020

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Summary Biological systems are composed of highly complex networks, and decoding the functional significance of individual network components is critical for understanding healthy and diseased states. Several algorithms have been designed to identify the most influential regulatory points within a network. However, current methods do not address all the topological dimensions of a network or correct for inherent positional biases, which limits their applicability. To overcome this computational deficit, we undertook a statistical assessment of 200 real-world and simulated networks to decipher associations between centrality measures and developed an algorithm termed Integrated Value of Influence (IVI), which integrates the most important and commonly used network centrality measures in an unbiased way. When compared against 12 other contemporary influential node identification methods on ten different networks, the IVI algorithm outperformed all other assessed methods. Using this versatile method, network researchers can now identify the most influential network nodes.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Integrated Value of Influence: An Integrative Method for the Identification of the Most Influential Nodes within Networks

2020

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“…The approach to conditionals outlined in Douven (2008Douven ( , 2016 employs the notion of evidential support from Bayesian epistemology. Wenmackers et al (2013), van Rooij and Schulz (2019), and Berto and Özgün (2020) provide further examples. While (9) can be paraphrased by means of sentences analogous to (4) and (5), the most appropriate reformulations of (10) are sentences analogous to (7) and (8).…”

Section: Overviewmentioning

confidence: 96%

The Evidential Conditional

Crupi

Iacona

2020

Erkenn

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This paper outlines an account of conditionals, the evidential account, which rests on the idea that a conditional is true just in case its antecedent supports its consequent. As we will show, the evidential account exhibits some distinctive logical features that deserve careful consideration. On the one hand, it departs from the material reading of ‘if then’ exactly in the way we would like it to depart from that reading. On the other, it significantly differs from the non-material accounts which hinge on the Ramsey Test, advocated by Adams, Stalnaker, Lewis, and others.

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“…9 The label 'inferentialism' is used more generally in the literature, to refer more or less to any account that emphasizes relevance as influencing the acceptability of conditionals. So used, the label would apply to the evidential support theory to be discussed below, to other approaches that handle relevance probabilistically, e.g., Skovgaard-Olsen et al (2016), or causally e.g., van Rooij and Schulz (2019), or by resorting to non-classical logics, e.g., Dunn and Restall (2002), and to our own view as well. But the label, however popular, is a misnomer, just as 'counterfactual' is a misnomer for the subjunctive (Lewis 1973;Williamson 2007).…”

Section: Inferentialism and Evidential Supportmentioning

confidence: 99%

Indicative conditionals: probabilities and relevance

Berto

Özgün

2021

Philos Stud

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We propose a new account of indicative conditionals, giving acceptability and logical closure conditions for them. We start from Adams’ Thesis: the claim that the acceptability of a simple indicative equals the corresponding conditional probability. The Thesis is widely endorsed, but arguably false and refuted by empirical research. To fix it, we submit, we need a relevance constraint: we accept a simple conditional $$\varphi \rightarrow \psi$$ φ → ψ to the extent that (i) the conditional probability $$\mathrm{p}(\psi |\varphi )$$ p ( ψ | φ ) is high, provided that (ii) $$\varphi$$ φ is relevant for $$\psi$$ ψ . How (i) should work is well-understood. It is (ii) that holds the key to improve our understanding of conditionals. Our account has (i) a probabilistic component, using Popper functions; (ii) a relevance component, given via an algebraic structure of topics or subject matters. We present a probabilistic logic for simple indicatives, and argue that its (in)validities are both theoretically desirable and in line with empirical results on how people reason with conditionals.

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Conditionals, Causality and Conditional Probability

Abstract: The appropriateness, or acceptability, of a conditional does not just 'go with' the corresponding conditional probability. A condition of dependence is required as well (cf.

Cited by 43 publications

References 41 publications

Integrated Value of Influence: An Integrative Method for the Identification of the Most Influential Nodes within Networks

Integrated Value of Influence: An Integrative Method for the Identification of the Most Influential Nodes within Networks

The Evidential Conditional

Indicative conditionals: probabilities and relevance

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