Liu, Chong
(2024)
A new theory of causation based on probability distribution determinism.
[Preprint]
Abstract
The concept of causation is essential for understanding relationships among various phenomena, yet its fundamental nature and the criteria for establishing it continue to be debated. This paper presents a new theory of causation through a quasi-axiomatic approach. The core of this framework is Probability Distribution Determinism (PDD), which updates traditional determinism by representing states of affairs as probability distributions, with the if-then function serving as its foundational definition. Based on PDD, by merely using appropriate naming strategies, it is possible to derive systems in which the structural characteristics of relationships among things closely resemble those in the real world, such as having various forms of nested hierarchies. Additionally, there are two related yet distinctly different contexts about relationships in PDD: one emphasizes the potential influence of conditions on outcomes in the general sense, while the other focuses on attributing responsibility for the state changes in specific scenarios. The formula for determining the relationship in the general sense is established as S(Y |S1(X), Ψ) ̸ ≡ S(Y |S2(X), Ψ). Subsequently, within the PDD framework, the paper clarifies the legitimate use of a series of concepts related to causation in those two contexts, thus encompassing the entire detailed connotation of the concept of causation. The comparison with other theories of causation and the analysis of case applications demonstrate that the new theory applies not only to situations where other theories are competent but also to situations where they are not. This suggests that, although certain aspects within the new framework may require further analysis, it provides a highly promising direction for a deeper understanding of causation.
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