Generalised Reichenbachian Common Cause Systems
The principle of the common cause claims that if an improbable coincidence has occurred, there must exist a common cause. This is generally taken to mean that positive correlations between non-causally related events should disappear when conditioning on the action of some underlying common cause. The extended interpretation of the principle, by contrast, urges that common causes should be called for in order to explain positive deviations between the estimated correlation of two events and the expected value of their correlation. The aim of this paper is to provide the extended reading of the principle with a general probabilistic model, capturing the simultaneous action of a system of multiple common causes. To this end, two distinct models are elaborated, and the necessary and sufficient conditions for their existence are determined.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Do Reichenbachian Common Cause Systems of Arbitrary Finite Size Exist?
The principle of common cause asserts that positive correlations between causally unrelated events ought to be explained through the action of some shared causal factors. Reichenbachian common cause systems are probabili…
On the Existence of One-Sided Representations for the Generalised Dynamic Factor Model
We consider the Generalised Dynamic Factor Model (GDFM) under the assumption that the dynamic common component is purely non-deterministic. We show that, under thisassumption, the common shocks - and consequently, the dy…
A Distributed Lag Approach to the Generalised Dynamic Factor Model (GDFM)
We provide estimation and inference for the Generalised Dynamic Factor Model (GDFM) under the assumption that the dynamic common component can be expressed in terms of a finite number of lags of contemporaneously pervasi…
regressionGeneralised Lotka-Volterra model with hierarchical interactions
In the analysis of complex ecosystems it is common to use random interaction coefficients, often assumed to be such that all species are statistically equivalent. In this work we relax this assumption by choosing interac…
modelLaGSplat: Inferring Physics-Governed Interactive Simulation from Monocular Video Using Latent Lagrangian Gaussian Splatting
We present LaGSplat (Latent Lagrangian Gaussian Splatting), a framework that infers interactive, physics-governed dynamics from one or a few monocular videos. At inference it lets a user push on the filmed object, rigid …