Our account provides a local, realist and fully non-causal principle explanation for EPR correlations, contextuality, no-signalling, and the Tsirelson bound. Indeed, the account herein is fully consistent with the causal structure of Minkowski spacet...
At the basis of the problem of explaining non-local quantum correlations lies the tension between two factors: on the one hand, the natural interpretation of correlations as the manifestation of a causal relation; on the other, the resistance on the...
Explainable artificial intelligence (XAI) has become a central methodology for developing transparent, accountable, and human-centered AI systems. As data-driven models are increasingly deployed in high-stakes and socially consequential settings, exp...
In this paper, we propose an application of critical thinking (CT) to real-world problems, taking into account personal well-being (PB) and lifelong formation (FO). First, we raise a substantial problem with CT, which is that causal explanation is of...
The common approach to integrating thermodynamics and economics is subsuming thermodynamic aspects among the set of constraints under which economic activity takes place. The causal link between energy and growth is investigated via aggregate econome...
In the context of the rapid advancement of financial technology, the issue of credit card default has become increasingly salient, emerging as one of the crucial risks that financial institutions are eagerly addressing. Traditional credit card defaul...
Explainable Artificial Intelligence (XAI) has emerged as a crucial research area to address the interpretability challenges posed by complex machine learning models. In this survey paper, we provide a comprehensive analysis of existing approaches in...
This study proposes a novel causality-structured matrix long short-term memory (C-mLSTM) model for significant wave height (SWH) forecasting. The framework incorporates a two-stage causal feature selection methodology using cointegration testing and...
The lack of transparency in many AI systems continues to hinder their adoption in critical domains such as healthcare, finance, and autonomous systems. While recent explainable AI (XAI) methods—particularly those leveraging large language model...
Modern explainable AI (XAI) methods remain far from providing human-like answers to ‘why’ questions, let alone those that satisfactorily agree with human-level understanding. Instead, the results that such methods provide boil down to set...
Background: Arthur Schopenhauer’s On the Fourfold Root of the Principle of Sufficient Reason (in its revised 1847 edition) has been variously interpreted as an epistemological prelude to his metaphysics of will, as a continuation of Kantian tra...
Human–elephant conflict (HEC) is a severe and much-debated issue in Sri Lanka. An average of two hundred animals are intentionally killed, and seventy to eighty human casualties are counted each year. The Sri Lankan elephant (Elephas maximus maximus)...
The rapid growth of research in explainable artificial intelligence (XAI) follows on two substantial developments. First, the enormous application success of modern machine learning methods, especially deep and reinforcement learning, have created hi...
More than one-third of adolescents experience recurrent headaches. Usually, these headaches are of primary origin and modulated by different biological and psychosocial factors. While parents are often consulted in scientific research and medical car...
For an AI agent to make trustworthy decision recommendations under uncertainty on behalf of human principals, it should be able to explain why its recommended decisions make preferred outcomes more likely and what risks they entail. Such rationales u...
Nowadays, Artificial Intelligence systems have expanded their competence field from research to industry and daily life, so understanding how they make decisions is becoming fundamental to reducing the lack of trust between users and machines and inc...
The last decades have seen a revolution in autonomous robotics. Deep learning approaches and their hardware implementations have made it possible to endow robots with extraordinary perceptual capabilities. In addition, they can benefit from advances...
Counterfactual explanations are increasingly vital for understanding and trusting machine learning models. This paper presents Desirability Rating-based Counterfactual (DeRaC), which is a generalized framework for generating valid counterfactual expl...
The Loess Plateau has experienced persistent vegetation greening over the past two decades, yet this recovery has occurred under a concurrent intensification of atmospheric evaporative demand and drying. This raises a key land–atmosphere questi...
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Prediction of high-risk individuals and the multi-risk approach are common inquiries in caries risk epidemiology. These studies prepared the ground for future studies; specific hypotheses about causal patterns can now be formulated and tested applyin...
Diagnosing faults across different smart homes is hard: sensor names, layouts, and daily routines differ from home to home, so a model trained in one home rarely works in another. We study an ontology-guided framework for cross-home fault diagnosis,...
At the end of 2023, the Whole Mouse Brain Atlas was announced, revealing that there are about 5300 molecularly defined neuronal types in the mouse brain. We ask whether brain models exist that contemplate how this is possible. The conventional column...
This paper naturalizes inductive inference by showing how scientific knowledge of real mechanisms provides large benefits to it. I show how knowledge about mechanisms contributes to generalization, inference to the best explanation, causal inference,...
Assume that, even with a time machine, Tim does not have the ability to travel to the past and kill Grandfather. Why would that be? And what are the implications for traditional debates about freedom? We argue that there are at least two satisfactory...
Explainable recommendation has been conceptualized as a joint ranking task encompassing both items and explanations within contemporary recommender system research. The modeling of user–item–explanation triplets can be effectively facilit...
Preeclampsia is a form of gestational hypertension that usually appears after the 20th week of pregnancy. Evidence suggests that low-dose aspirin (LDA) effectively reduces the risk of developing preeclampsia, but the uptake rate remains low. This sco...
A plausible thought is that we should evaluate counterfactuals in the actual world by holding the present ‘fixed’; the state of the counterfactual world at the time of the antecedent, outside the area of the antecedent, is required to mat...
Recent efforts have uncovered various methods for providing explanations that can help interpret the behavior of machine learning programs. Exact explanations with a rigorous logical foundation provide valid and complete explanations, but they have a...
Multimorbidity risk identification in elderly health consultation scenarios is challenging because chronic disease history and medication exposure may interact through complex causal pathways. Existing LLM-, RAG-, and graph-based retrieval methods of...
Accurate shape control is paramount for ensuring the quality of hot-rolled strip products, which is significantly challenged by the high dimensionality, inherent nonlinearity, and strong coupling of process parameters. While machine learning (ML) met...
In a paper published in 1939, Albert Einstein argued that Black Holes (BHs) did not exist “in the real world”. However, recent astronomical observations indicate otherwise. Does this mean that we should also expect White Holes (WHs) to ex...
The main goal of scientific research is to explain what causes a phenomenon. However, only well-controlled studies guarantee sufficient internal validity to support causal explanations (i.e., experimental and some quasi-experimental designs). The use...
We propose a novel interpretability framework that integrates instance-wise feature selection with causal reasoning to explain decisions made by black-box image classifiers. Instead of relying on feature importance or mutual information, our method i...
The conventional explanation of delayed-choice experiments appears to violate our causal intuition at the quantum level. I reanalyze these experiments using time-reversed and time-symmetric formulations of quantum mechanics. The time-reversed formula...
Artificial intelligence (AI) systems for intensive care units (ICUs) must support early risk prediction while producing explanations that clinicians can inspect, question, and relate to physiological reasoning. Deep neural networks (DNNs) can learn c...
Information geometry has offered a way to formally study the efficacy of scientific models by quantifying the impact of model parameters on the predicted effects. However, there has been little formal investigation of causation in this framework, des...
Conventional machine-learning-based defenses are unable to generalize well to novel chains of ATT&CK actions. Being inefficient with low telemetry budgets, they are also unable to provide causal explainability and auditing. We propose a knowledge...
Accurate carbon price forecasting is essential for emission trading risk management and low-carbon investment decisions. In existing decomposition-prediction frameworks, secondary decomposition targets are typically selected based on statistical comp...
We seem to be able to acquire evaluative knowledge by mere reflection, or “from the armchair.” But how? This question is especially pressing for proponents of sentimental perceptualism, which is the view that our evaluative knowledge is r...
Most frameworks used in the management of environmental problems focus on problem analysis and pay little or no attention to the explanation of the problem and the opportunities for solving it. The Opportunity and Problem in Context (OPiC) framework...
Rotating machinery is essential to energy, aerospace, manufacturing, and other safety-critical industries. Although deep-learning-based fault diagnosis has achieved high accuracy, its black-box nature limits trust, verification, and industrial deploy...
Patients with cancer have been reported to show elevated plasma concentrations of vitamin B12, thus causing uncertainties regarding safety of vitamin B12. We conducted a systematic literature search and a scoping review of human studies published in...
Causal analysis can guide decisions by estimating how interventions change outcomes and making the assumptions behind those estimates explicit. We present the Causal World Foundation Model, a framework for answering causal questions from tabular data...
Recent benchmarks increasingly report that large language models (LLMs) exhibit human-like causal reasoning abilities, including counterfactual inference and intervention planning. However, many such evaluations rely on domains that are heavily repre...
Large language models (LLMs) have shown their capabilities in numerical and logical reasoning, yet their capabilities in higher-order cognitive tasks, particularly causal reasoning, remain less explored. Current research on LLMs in causal reasoning h...
Large Language Models (LLMs) are increasingly used in industrial monitoring and decision support, yet they remain prone to process-control hallucinations—diagnoses and explanations that sound plausible but conflict with physical constraints, se...
Large Language Models (LLMs) excel in diverse text generation tasks but still face limited controllability, opaque decision processes, and frequent hallucinations. This paper presents a structural causal intervention framework that models input&ndash...
A path analysis method for causal systems based on generalized linear models is proposed by using entropy. A practical example is introduced, and a brief explanation of the entropy coefficient of determination is given. Direct and indirect effects of...
Cities around the world are betting on sustainable transitions as a formula to respond to some of the challenges they face. Within transitions, the acceleration phase has been little studied, perhaps because it relies on the mechanisms linked to the...
Quantum physics can only make statistical predictions about possible measurement outcomes, and these predictions originate from an operator algebra that is fundamentally different from the conventional definition of probability as a subjective lack o...
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