Characterizing Signalling: Connections between Causal Inference and Space-time Geometry
- URL: http://arxiv.org/abs/2403.00916v2
- Date: Fri, 16 Aug 2024 13:52:00 GMT
- Title: Characterizing Signalling: Connections between Causal Inference and Space-time Geometry
- Authors: Maarten Grothus, V. Vilasini,
- Abstract summary: Causality is pivotal to our understanding of the world, presenting itself in different forms: information-theoretic and relativistic.
We use a framework introduced in PRA, 106, 032204 (2022), which formally connects these two notions in general physical theories.
We study the embedding of information-theoretic causal models in space-time without violating relativistic principles.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Causality is pivotal to our understanding of the world, presenting itself in different forms: information-theoretic and relativistic, the former linked to the flow of information, the latter to the structure of space-time. Leveraging a framework introduced in PRA, 106, 032204 (2022), which formally connects these two notions in general physical theories, we study their interplay. Here, information-theoretic causality is defined through a causal modelling approach. First, we improve the characterization of information-theoretic signalling as defined through so-called affects relations. Specifically, we provide conditions for identifying redundancies in different parts of such a relation, introducing techniques for causal inference in unfaithful causal models (where the observable data does not "faithfully" reflect the causal dependences). In particular, this demonstrates the possibility of causal inference using the absence of signalling between certain nodes. Second, we define an order-theoretic property called conicality, showing that it is satisfied for light cones in Minkowski space-times with $d>1$ spatial dimensions but violated for $d=1$. Finally, we study the embedding of information-theoretic causal models in space-time without violating relativistic principles such as no superluminal signalling (NSS). In general, we observe that constraints imposed by NSS in a space-time and those imposed by purely information-theoretic causal inference behave differently. We then prove a correspondence between conical space-times and faithful causal models: in both cases, there emerges a parallel between these two types of constraints. This indicates a connection between informational and geometric notions of causality, and offers new insights for studying the relations between the principles of NSS and no causal loops in different space-time geometries and theories of information processing.
Related papers
- New Rules for Causal Identification with Background Knowledge [59.733125324672656]
We propose two novel rules for incorporating BK, which offer a new perspective to the open problem.
We show that these rules are applicable in some typical causality tasks, such as determining the set of possible causal effects with observational data.
arXiv Detail & Related papers (2024-07-21T20:21:21Z) - A causal modelling analysis of Bell scenarios in space-time:
implications of jamming non-local correlations for relativistic causality
principles [1.0878040851638]
Bell scenarios involve space-like separated measurements made by multiple parties.
Non-signalling constraints have been proposed, which permit a class of post-quantum theories known as jamming non-local theories.
We show that any theory that generates jamming correlations in a Bell scenario must necessarily do so through causal fine-tuning and by means of superluminal causal influences.
arXiv Detail & Related papers (2023-11-30T11:17:49Z) - Relativity of spacetime ontology: When correlations in space become
correlata in time [0.0]
We argue that correlat and correlationsa are not fundamentally distinct.
Since the same quantum states may be either entangled or separable, a spatial correlation in one context can become a temporal correlatum in another.
arXiv Detail & Related papers (2023-11-23T09:58:50Z) - Nonparametric Identifiability of Causal Representations from Unknown
Interventions [63.1354734978244]
We study causal representation learning, the task of inferring latent causal variables and their causal relations from mixtures of the variables.
Our goal is to identify both the ground truth latents and their causal graph up to a set of ambiguities which we show to be irresolvable from interventional data.
arXiv Detail & Related papers (2023-06-01T10:51:58Z) - Compatibility of Cyclic Causal Structures with Spacetime in General
Theories with Free Interventions [0.0]
We show the possibility of operationally detectable causal loops embedded in (1+1)-Minkowski spacetime without superluminal signalling.
We establish new properties of HO affects relations and apply them to infer causal structures.
We study the embedding of information-theoretic causal structures into partially ordered spacetimes.
arXiv Detail & Related papers (2022-11-07T14:30:03Z) - Learning latent causal relationships in multiple time series [0.0]
In many systems, the causal relations are embedded in a latent space that is expressed in the observed data as a linear mixture.
A technique for blindly identifying the latent sources is presented.
The proposed technique is unsupervised and can be readily applied to any multiple time series to shed light on the causal relationships underlying the data.
arXiv Detail & Related papers (2022-03-21T00:20:06Z) - Effect Identification in Cluster Causal Diagrams [51.42809552422494]
We introduce a new type of graphical model called cluster causal diagrams (for short, C-DAGs)
C-DAGs allow for the partial specification of relationships among variables based on limited prior knowledge.
We develop the foundations and machinery for valid causal inferences over C-DAGs.
arXiv Detail & Related papers (2022-02-22T21:27:31Z) - A general framework for cyclic and fine-tuned causal models and their
compatibility with space-time [2.0305676256390934]
Causal modelling is a tool for generating causal explanations of observed correlations.
Existing frameworks for quantum causality tend to focus on acyclic causal structures that are not fine-tuned.
Cyclist causal models can be used to model physical processes involving feedback.
Cyclist causal models may also be relevant in exotic solutions of general relativity.
arXiv Detail & Related papers (2021-09-24T18:00:08Z) - The Causal Neural Connection: Expressiveness, Learnability, and
Inference [125.57815987218756]
An object called structural causal model (SCM) represents a collection of mechanisms and sources of random variation of the system under investigation.
In this paper, we show that the causal hierarchy theorem (Thm. 1, Bareinboim et al., 2020) still holds for neural models.
We introduce a special type of SCM called a neural causal model (NCM), and formalize a new type of inductive bias to encode structural constraints necessary for performing causal inferences.
arXiv Detail & Related papers (2021-07-02T01:55:18Z) - Disentangling Observed Causal Effects from Latent Confounders using
Method of Moments [67.27068846108047]
We provide guarantees on identifiability and learnability under mild assumptions.
We develop efficient algorithms based on coupled tensor decomposition with linear constraints to obtain scalable and guaranteed solutions.
arXiv Detail & Related papers (2021-01-17T07:48:45Z) - Causal Expectation-Maximisation [70.45873402967297]
We show that causal inference is NP-hard even in models characterised by polytree-shaped graphs.
We introduce the causal EM algorithm to reconstruct the uncertainty about the latent variables from data about categorical manifest variables.
We argue that there appears to be an unnoticed limitation to the trending idea that counterfactual bounds can often be computed without knowledge of the structural equations.
arXiv Detail & Related papers (2020-11-04T10:25:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.