Diagrammatic Modelling of Causality and Causal Relations
- URL: http://arxiv.org/abs/2310.11042v1
- Date: Tue, 17 Oct 2023 07:17:51 GMT
- Title: Diagrammatic Modelling of Causality and Causal Relations
- Authors: Sabah Al-Fedaghi
- Abstract summary: This paper concerns diagrammatical (graphic) models of causal relationships.
We experiment with using the conceptual language of thinging machines (TMs) as a tool in this context.
The results show that the TM depiction of causality is more complete and therefore can provide a foundation for causal graphs.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: It has been stated that the notion of cause and effect is one object of study
that sciences and engineering revolve around. Lately, in software engineering,
diagrammatic causal inference methods (e.g., Pearl s model) have gained
popularity (e.g., analyzing causes and effects of change in software
requirement development). This paper concerns diagrammatical (graphic) models
of causal relationships. Specifically, we experiment with using the conceptual
language of thinging machines (TMs) as a tool in this context. This would
benefit works on causal relationships in requirements engineering, enhance our
understanding of the TM modeling, and contribute to the study of the
philosophical notion of causality. To specify the causality in a system s
description is to constrain the system s behavior and thus exclude some
possible chronologies of events. The notion of causality has been studied based
on tools to express causal questions in diagrammatic and algebraic forms.
Causal models deploy diagrammatic models, structural equations, and
counterfactual and interventional logic. Diagrammatic models serve as a
language for representing what we know about the world. The research
methodology in the paper focuses on converting causal graphs into TM models and
contrasts the two types of representation. The results show that the TM
depiction of causality is more complete and therefore can provide a foundation
for causal graphs.
Related papers
- Emergence and Causality in Complex Systems: A Survey on Causal Emergence
and Related Quantitative Studies [12.78006421209864]
Causal emergence theory employs measures of causality to quantify emergence.
Two key problems are addressed: quantifying causal emergence and identifying it in data.
We highlighted that the architectures used for identifying causal emergence are shared by causal representation learning, causal model abstraction, and world model-based reinforcement learning.
arXiv Detail & Related papers (2023-12-28T04:20:46Z) - Interpretable Imitation Learning with Dynamic Causal Relations [65.18456572421702]
We propose to expose captured knowledge in the form of a directed acyclic causal graph.
We also design this causal discovery process to be state-dependent, enabling it to model the dynamics in latent causal graphs.
The proposed framework is composed of three parts: a dynamic causal discovery module, a causality encoding module, and a prediction module, and is trained in an end-to-end manner.
arXiv Detail & Related papers (2023-09-30T20:59:42Z) - Causal models in string diagrams [0.0]
The framework of causal models provides a principled approach to causal reasoning, applied today across many scientific domains.
We present this framework in the language of string diagrams, interpreted formally using category theory.
We argue and demonstrate that causal reasoning according to the causal model framework is most naturally and intuitively done as diagrammatic reasoning.
arXiv Detail & Related papers (2023-04-15T21:54:48Z) - CLEAR: Generative Counterfactual Explanations on Graphs [60.30009215290265]
We study the problem of counterfactual explanation generation on graphs.
A few studies have explored counterfactual explanations on graphs, but many challenges of this problem are still not well-addressed.
We propose a novel framework CLEAR which aims to generate counterfactual explanations on graphs for graph-level prediction models.
arXiv Detail & Related papers (2022-10-16T04:35:32Z) - Markov categories, causal theories, and the do-calculus [7.061298918159947]
We give a category-theoretic treatment of causal models that formalizes the syntax for causal reasoning over a directed acyclic graph (DAG)
This framework enables us to define and study important concepts in causal reasoning from an abstract and "purely causal" point of view.
arXiv Detail & Related papers (2022-04-11T01:27:41Z) - CausalKG: Causal Knowledge Graph Explainability using interventional and
counterfactual reasoning [0.0]
Humans use causality and hypothetical retrospection in their daily decision-making, planning, and understanding of life events.
It develops a causal model of the world, which learns with fewer data points, makes inferences, and contemplates counterfactual scenarios.
The proposed Causal Knowledge Graph (CausalKG) framework, leverages recent progress of causality and KG towards explainability.
arXiv Detail & Related papers (2022-01-06T20:27:19Z) - Systematic Evaluation of Causal Discovery in Visual Model Based
Reinforcement Learning [76.00395335702572]
A central goal for AI and causality is the joint discovery of abstract representations and causal structure.
Existing environments for studying causal induction are poorly suited for this objective because they have complicated task-specific causal graphs.
In this work, our goal is to facilitate research in learning representations of high-level variables as well as causal structures among them.
arXiv Detail & Related papers (2021-07-02T05:44:56Z) - Towards Causal Representation Learning [96.110881654479]
The two fields of machine learning and graphical causality arose and developed separately.
There is now cross-pollination and increasing interest in both fields to benefit from the advances of the other.
arXiv Detail & Related papers (2021-02-22T15:26:57Z) - Fuzzy Stochastic Timed Petri Nets for Causal properties representation [68.8204255655161]
Causal relations are frequently represented by directed graphs, with nodes denoting causes and links denoting causal influence.
Common methods used for graphically representing causal scenarios are neurons, truth tables, causal Bayesian networks, cognitive maps and Petri Nets.
We will show that, even though the traditional models are able to represent separately some of the properties aforementioned, they fail trying to illustrate indistinctly all of them.
arXiv Detail & Related papers (2020-11-24T13:22:34Z) - Causal Discovery in Physical Systems from Videos [123.79211190669821]
Causal discovery is at the core of human cognition.
We consider the task of causal discovery from videos in an end-to-end fashion without supervision on the ground-truth graph structure.
arXiv Detail & Related papers (2020-07-01T17:29:57Z)
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.