Causal Sufficiency and Actual Causation

Beckers, Sander

doi:10.1007/s10992-021-09601-z

Cited by 12 publications

(13 citation statements)

References 15 publications

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“…This problem is the primary focus of the copious literature on "actual causality", as famously laid out in a pair of influential articles by Halpern andPearl (2005a, 2005b), and later given booklength treatment in a monograph by Halpern (2016). For a recent survey and refinement of the formal definitions, see Beckers (2021). The common thread in all these works, cashed out in various ways by philosophers including Mackie (1965) and Wright (2013), is that x causes y iff x is a necessary element of a sufficient set for y.…”

Section: Necessity and Sufficiencymentioning

confidence: 99%

Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice

et al. 2022

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Necessity and sufficiency are the building blocks of all successful explanations. Yet despite their importance, these notions have been conceptually underdeveloped and inconsistently applied in explainable artificial intelligence (XAI), a fast-growing research area that is so far lacking in firm theoretical foundations. In this article, an expanded version of a paper originally presented at the 37th Conference on Uncertainty in Artificial Intelligence (Watson et al., 2021), we attempt to fill this gap. Building on work in logic, probability, and causality, we establish the central role of necessity and sufficiency in XAI, unifying seemingly disparate methods in a single formal framework. We propose a novel formulation of these concepts, and demonstrate its advantages over leading alternatives. We present a sound and complete algorithm for computing explanatory factors with respect to a given context and set of agentive preferences, allowing users to identify necessary and sufficient conditions for desired outcomes at minimal cost. Experiments on real and simulated data confirm our method’s competitive performance against state of the art XAI tools on a diverse array of tasks.

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Section: Necessity and Sufficiencymentioning

confidence: 99%

Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice

et al. 2022

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“…It should be noted that with a strict interpretation, the theoretical assumptions for enabling CD, cannot be satisfied for a complex real-world system. For a detailed discussion on assumptions and their limitations, refer to [5], [4], [18]. A traditional way to discover causal relations is to use interventions or randomized experiments, which is in many cases too expensive, time-consuming, or even impossible [19].…”

Section: A Assumptionsmentioning

confidence: 99%

CIPCaD-Bench: Continuous Industrial Process datasets for benchmarking Causal Discovery methods

Menegozzo¹,

Dall’Alba²,

Fiorini³

2022

Preprint

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Causal relationships are commonly examined in manufacturing processes to support faults investigations, perform interventions, and make strategic decisions. Industry 4.0 has made available an increasing amount of data that enable data-driven Causal Discovery (CD). Considering the growing number of recently proposed CD methods, it is necessary to introduce strict benchmarking procedures on publicly available datasets since they represent the foundation for a fair comparison and validation of different methods. This work introduces two novel public datasets for CD in continuous manufacturing processes. The first dataset employs the well-known Tennessee Eastman simulator for fault detection and process control. The second dataset is extracted from an ultra-processed food manufacturing plant, and it includes a description of the plant, as well as multiple ground truths. These datasets are used to propose a benchmarking procedure based on different metrics and evaluated on a wide selection of CD algorithms. This work allows testing CD methods in realistic conditions enabling the selection of the most suitable method for specific target applications. The datasets are available at the following link: [https://github.com/giovanniMen]

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“…67 [Halpern, 2016] writes that "(his) current feeling is that type causation arises from many instances of actual causation, so that actual causation is more fundamental (...)". In a recent paper, [Beckers, 2021a] gives twelve new definitions of actual causality.…”

Section: Semantic Translationsmentioning

confidence: 99%

“…For our work, however, we need to go the other direction and understand the notion of causality in a source model. For this, we can use the work of [Beckers, 2021a], who observes that we can use Pearl's definition of causal sufficiency to derive definitions of causality. If we have a causal model M = ((U, V, R), F), we say that a setting X = x is sufficient for a setting Y = y if Y "follows" from X .…”

Section: Semantic Translationsmentioning

confidence: 99%

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Accountability for Cyber- Physical Systems

Pretschner

Kacianka

2021

Encyclopedia of Cryptography, Security and Privacy

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First and foremost I would like to thank my mother Andrea and my sister Nike for their unwavering support throughout my whole life and especially during the writing of this thesis. Thank you for lifting me up when I was down, for pushing me when I was lazy and for proof-reading every word I ever wrote.Next and most importantly, I want to thank my supervisor Alexander Pretschner.Through this joint journey, you taught me how to get to the point, to tell a good story, and to be a decent researcher and human being. You had my back when I did mess up and supported me when I tried something new. I am especially grateful for the honest and tough discussions -they made this text and all my work so much better. Thank you for shaping my view of science, good work, and my philosophy of life.A special thanks goes to all my colleagues past and present. You made the chair something I was looking forward to almost every single day. We made so many wonderful memories, shared stories and insights. All of you shaped my thinking and attitude and I am a better man for it. I am proud to have worked with you and grateful to be able to consider you friends. Thank you.Last, but not least, I want to thank my friends for their help, support and especially for the time not spent on the thesis. Anastasia, for making this last leg of my PhD so special; Celia, Eva, Evi, Hannes, Luis, Marci, Maresa, Michael, Peer, and Philip for those countless hikes and climbs that kept me sane; Niina and Jan for our project and the discussions that inspired me so much; Anne, Corinna, Luisa, Markus, and Nikolaus for those long nights and deep talks.

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Causal Sufficiency and Actual Causation

Cited by 12 publications

References 15 publications

Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice

Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice

CIPCaD-Bench: Continuous Industrial Process datasets for benchmarking Causal Discovery methods

Accountability for Cyber- Physical Systems

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