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Counterfactuals have been recognized as an effective approach to explain classifier decisions. Nevertheless, they have not yet been considered in the context of clustering. In this work, we propose the use of counterfactuals to explain…

Machine Learning · Computer Science 2025-01-20 Georgios Vardakas , Antonia Karra , Evaggelia Pitoura , Aristidis Likas

Correlations between spacelike separated measurements on entangled quantum systems are stronger than any classical correlations and are at the heart of numerous quantum technologies. In practice, however, spacelike separation is often not…

Quantum Physics · Physics 2017-11-28 Martin Ringbauer , Rafael Chaves

Evaluating hypothetical statements about how the world would be had a different course of action been taken is arguably one key capability expected from modern AI systems. Counterfactual reasoning underpins discussions in fairness, the…

Machine Learning · Computer Science 2022-10-04 Kevin Xia , Yushu Pan , Elias Bareinboim

It has recently been found that Bell scenarios are only a small subclass of interesting setups for studying the non-classical features of quantum theory within spacetime. We find that it is possible to talk about classical correlations,…

Quantum Physics · Physics 2016-01-12 Tobias Fritz

Parametric causal modelling techniques rarely provide functionality for counterfactual estimation, often at the expense of modelling complexity. Since causal estimations depend on the family of functions used to model the data, simplistic…

Machine Learning · Statistics 2020-06-16 Álvaro Parafita , Jordi Vitrià

In general relativity, the causal structure between events is dynamical, but it is definite and observer-independent; events are point-like and the membership of an event A in the future or past light-cone of an event B is an…

Quantum Physics · Physics 2020-07-13 Philippe Allard Guérin , Časlav Brukner

Counterfactual reasoning is pivotal in human cognition and especially important for providing explanations and making decisions. While Judea Pearl's influential approach is theoretically elegant, its generation of a counterfactual scenario…

Artificial Intelligence · Computer Science 2024-11-01 Guang-Yuan Hao , Jiji Zhang , Biwei Huang , Hao Wang , Kun Zhang

We analyze complexity of financial (and general economic) processes by comparing classical and quantum-like models for randomness. Our analysis implies that it might be that a quantum-like probabilistic description is more natural for…

Statistical Finance · Quantitative Finance 2014-03-13 Andrei Khrennikov

The theory of actual causality, defined by Halpern and Pearl, and its quantitative measure - the degree of responsibility - was shown to be extremely useful in various areas of computer science due to a good match between the results it…

Software Engineering · Computer Science 2016-08-30 Hana Chockler

A careful study of the classical/quantum connection with the aid of coherent states offers new insights into various technical problems. This analysis includes both canonical as well as closely related affine quantization procedures. The…

High Energy Physics - Theory · Physics 2015-06-12 John R. Klauder

We discuss the problem of bounding partially identifiable queries, such as counterfactuals, in Pearlian structural causal models. A recently proposed iterated EM scheme yields an inner approximation of those bounds by sampling the…

Artificial Intelligence · Computer Science 2023-10-06 David Huber , Yizuo Chen , Alessandro Antonucci , Adnan Darwiche , Marco Zaffalon

Evaluation of counterfactual queries (e.g., "If A were true, would C have been true?") is important to fault diagnosis, planning, and determination of liability. In this paper we present methods for computing the probabilities of such…

Artificial Intelligence · Computer Science 2013-02-28 Alexander Balke , Judea Pearl

Counterfactual inference is a useful tool for comparing outcomes of interventions on complex systems. It requires us to represent the system in form of a structural causal model, complete with a causal diagram, probabilistic assumptions on…

This paper presents a model of contrastive explanation using structural casual models. The topic of causal explanation in artificial intelligence has gathered interest in recent years as researchers and practitioners aim to increase trust…

Artificial Intelligence · Computer Science 2023-06-22 Tim Miller

In this work, we give rigorous operational meaning to superposition of causal orders. This fits within a recent effort to understand how the standard operational perspective on quantum theory could be extended to include indefinite…

Quantum Physics · Physics 2021-09-07 Nicola Pinzani , Stefano Gogioso

Within quantum theory, we can create superpositions of different causal orders of events, and observe interference between them. This raises the question of whether quantum theory can produce results that would be impossible to replicate…

Quantum Physics · Physics 2021-10-07 Tom Purves , Anthony J. Short

In this paper, following an elementary line of thought which somewhat differs from the usual one, we prove once more that any deterministic theory predictively equivalent to quantum mechanics unavoidably exhibits a contextual character. The…

Quantum Physics · Physics 2009-11-13 GianCarlo Ghirardi , Karl Wienand

Since Pearl's seminal work on providing a formal language for causality, the subject has garnered a lot of interest among philosophers and researchers in artificial intelligence alike. One of the most debated topics in this context regards…

Artificial Intelligence · Computer Science 2015-10-30 Sander Beckers , Joost Vennekens

I distinguish two types of reduction within the context of quantum-classical relations, which I designate "formal" and "empirical". Formal reduction holds or fails to hold solely by virtue of the mathematical relationship between two…

Quantum Physics · Physics 2015-11-23 Joshua Rosaler

To construct interpretable explanations that are consistent with the original ML model, counterfactual examples---showing how the model's output changes with small perturbations to the input---have been proposed. This paper extends the work…

Machine Learning · Computer Science 2020-06-16 Divyat Mahajan , Chenhao Tan , Amit Sharma
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