We acknowledge that not all counterfactual explanations may be feasible for a user. They have featured in metaphysical theories of causation, supervenience, grounding, ontological dependence, and dispositions. How Can You Be Content With a Counterfactual Explanation ... What Is Counterfactual Analysis? - HDS Luk a s Syk ora and Tom a s Kliegr Department of Information and Knowledge Engineering, Faculty of Informatics and Statistics, University of Economics, Prague fxsykl04, tomas.kliegrg@vse.cz Abstract. Counterfactuals (Stanford Encyclopedia of Philosophy) A counterfactual explanation may justify a rejected loan application as follows: Your application was denied because your annual income is $30,000 and your current balance is $200. Counterfactual conditionals (also subjunctive or X-marked) are conditional sentences which discuss what would have been true under different circumstances, e.g. That is, one needs a controlled setting where it is possible to compute the difference between an actual observed text, and what the text would have been had a . To The task of finding a counterfactual explanation . FACE: Feasible and Actionable Counterfactual Explanations. Some of these articles below give one part of the deception but aren't aware of other . According to an increasingly popular view among philosophers of science, both causal and non-causal explanations can be accounted for by a single theory: the counterfactual theory of explanation. Using this approach, we first consider four kinds of counterfactuals: necessary condition counterfactuals, SUIN condition counterfactuals, sufficient condition counterfactuals, and INUS condition counterfactuals. The generated samples can act as a recommendation for end-users to achieve their desired outputs. Counterfactual explanations (CE) provide means for prescriptive model explanations by suggesting actionable feature changes to allow individuals to achieve favorable outcomes in the future. There are 17 words that need to be removed from the document so that the predicted topic would no longer be "Medical," meaning there is quite some evidence that explains the model's decision. In the folder "01_KNIME_Workflows" you can find the example workflows to explain predictions in KNIME from Keras and . The Framework for counterfactual explanation is described in Fig 1. Both look for minimal changes, although the latter looks for a more constrained change (additions), to the input for the decision of the . counterfactual or contrastive explanation [36, 8, 24]. By contrast, the . Such explanations are certainly useful to a person facing the decision, but they are also useful to system builders and evaluators in debugging the algorithm. Troubleshooting, for example, often Dear Readers, Here is a collection of articles from Global Research documenting the Covid Deception that has been fostered on the Western World. 1. title = "A counterfactual approach to explanation in mathematics", abstract = "Our goal in this paper is to extend counterfactual accounts of scientific explanation to mathematics. A counterfactual explanation of a decision is the smallest set of relevant factors such that changing can alter Counterfactual thinking is a concept in psychology that involves the human tendency to create possible alternatives to life events that have already occurred; something that is contrary to what actually happened. While this sounds simple, there are several challenges in setting up and solving this optimization problem. OSF Storage (United States) _Action Effect_preprint.pdf. However, because of the nonlinearity of the DNN, the decisions made by DNNs are hardly interpretable. In the context of a machine learning classifier X would be an instance of interest and Y would be the label predicted by the model. The official Covid narrative is a total lie. If your income had instead been $35,000 and your current balance had been $400 and all other values remained constant, your application would have been approved . Challenges. INTRODUCTION. Event listing from University of Pittsburgh: Friday, December 3 from 12:30 PM to 1:00 PM Speaker: Nihal MuraliPaper Title: Augmentation by Counterfactual Explanation - Fixing an Overconfident . PaulCraigRoberts.org. They consider the following counterfactual statement (Baron, Colyvan, and . A COUNTERFACTUAL APPROACH TO EXPLANATION IN MATHEMATICS 3 our attention back to proofs and show that patterns of counterfactual dependence in the case of intra-mathematical explanation at issue re ect di erences between explanatory and non-explanatory proofs (x4). Counterfactual Explanation (Python) This KNIME Hub Space is dedicated to example workflows and additional files for the verified component "Counterfactual Explanation (Python)" available here: kni.me/c/wpVF3wtKLnH5V-IR . Updated on Sep 18. The counterfactual explanation that explains why document 01's predicted topic is Medical is shown in Figure 2b. On the counterfactual explanation, it is the tendency to consider counterfactuals that engenders the difference in causal judgments for actions and inactions. A counterfactual explanation of an outcome or a situation Y takes the form "If X had not occured, Y would not have occured" ( Interpretable Machine Learning ). Counterfactuals and Explanation BORIS KMENT On the received view, counterfactuals are analyzed using the concept of closeness between possible worlds: The counterfactual ‗If it had been the case that p, then it would have been the case that q' is true at a world w just in case q is true at all the possible p-worlds closest to w. The program . Definition and explanation. In summary, our work provides the following contributions: (i) an extensive benchmark of 11 popular counterfactual explanation methods, (ii) a benchmarking framework for research on future counterfactual explanation methods, and (iii) a standardized set of integrated evaluation measures and data sets for transparent and extensive comparisons of . Explanations are Selected Explanations provided are a subset from a possibly infinite set of explanations, based on a certain set of cognitive biases. Our insouciance and inaction are allowing the net of deception to close us in and prevent our resistance. How Can You Be Content With a Counterfactual Explanation of an Orchestrated "Pandemic"? Raw Data. Intro. PermuteAttack: Counterfactual Explanation of Machine Learning Credit Scorecards. Proximity - It refers to the difference between the actual input and the counterfactual instance. Paul Craig Roberts. Guest Post by Paul Craig Roberts. Counterfactual analysis (or counterfactual thinking) explores outcomes that did not actually occur, but which could have occurred under different conditions.It's a kind of what if? Hashemi, Masoud. We offer a basic counterfactual theory of . Such explanations are more desirable in expert domains. Work in Counterfactual Explanations tends to focus on the principle of "the closest possible world" that identifies small changes leading to the desired outcome. Given a perturbation vector, the user can interpret it as an action . It is of crucial importance to bridge the gap between theoretical approaches to contrastive and counterfactual explanation and the corresponding computational frameworks. Deep neural networks (DNNs) can accurately decode task-related information from brain activations. Also, there is usually more than just one counterfactual explanation of the same quality and length, and choosing the most suitable one remains an open research question. Obtaining counterfactual explanation involves identifying the closest point to the input at hand that results in a different prediction. Reutlinger (Eur J Philos Sci 7(2):239-256, 2017 . In this second edition of Counterfactuals and Causal Inference, completely revised and expanded, the essential features of the counterfactual approach to observational data analysis are presented with examples from the social, demographic, and health sciences. By providing a simplified explanation of the counterfactual principles of exchangeability, and consequences of nonexchangeability, this article aims to increase understanding of confounding on a conceptual level as well as the rationale underlying design and analytic strategies for dealing with confounding in (clinical) epidemiological research. The deception that is being forced on people . This is posted as a reference to a lot of good articles. a counterfactual explanation could be an attempt to game the FICO credit scoring algorithm3, which is kept as a trade secret to avoid just that. CFR is a self-play algorithm: it learns to play a game by repeatedly playing against itself. Furthermore, we introduce the concept of "region-targeted counterfactual explanations", and a corresponding framework, where users can guide the . Introduction Counterfactual thoughts are of interest to psychologists because people consider hypothetical situations as part of many different mental activities — planning, decision making, and problem solving, to name just a few. Leveraging recent semantic-to-image models, we propose a new generative counterfactual explanation framework that produces plausible and sparse modifications which preserve the overall scene structure. Most counterfactual explanation methods only operate on single input data. From Paul Craig Roberts at paulcraigroberts.org: Dear Readers, Here is a collection of articles from Global Research documenting the Covid Deception that has been fostered on the Western World. analysis and is a useful way for testing cause-and-effect relationships.. The past two decades have witnessed an explosion of interest in counterfactual accounts of causal scientific explanation. Finally, we extend our discussion of However, an action plored counterfactual visual explanations from the perspec-tive of counterfactual thinking. While this sounds simple, there are several challenges in setting up and solving this optimization problem. II. In this paper, we develop a set-theoretic and possible worlds approach to counterfactual analysis in case-study explanation. From perturbed data, we can interpret that the pre-trained . Pull requests. in the conditional distribution of Y given X. Counterfactual analysis consists of evaluating the e ects of such changes. The basic idea of counterfactual theories of causation is that the meaning of causal claims can be explained in terms of counterfactual conditionals of the form "If A had not occurred, C would not have occurred". There can be cases where the number of features that need to be changed may be too numerous, or the changes . Counterfactual explanation is one popular approach to explaining the behaviour of a deep neural network, which works by systematically answering the question "How would the model output change if the inputs were changed in this way?". By Paul Craig Roberts. Woodward's account of explanation, involving both counterfactual and causal information, is Janus-faced in this way, but it is largely harmonious duetoWoodward'sðnonreductive,circularÞcounterfactual'analysis'ofcau-sation, allowing explanatory modal information to be often interpreted as causal information. Counterfactual explanation (CE) is a post-hoc explanation for machine learning model that recommends an action as the amount of change in attribute values (a perturbation vector) necessary in order to change a prediction result of the model, as illustrated in Figure 1. (2013) for counterfactual analysis. Counterfactual thinking is, as it states: "counter to the facts". _READ ME.pdf. Plan for Experiment Sb. How Can You Be Content With a Counterfactual Explanation of an Orchestrated "Pandemic"? These counterfactual explanations describe the smallest change to the world that can be made to obtain a desirable outcome, or to arrive at the "closest possible world." As multiple variables or sets of variables can lead to one or more desirable outcomes, multiple counterfactual explanations can be provided, corresponding to different . You take Right Ave and encounter lots of traffic. Counterfactual vs Contrastive Explanations: As defined in (Counterfactual explanations without opening the black box: Automated decisions and the GDPR [17]) counterfactual explanations have little difference from contrastive explanations as defined in [4]. The Diverse Counterfactual Explanation approach seeks to provide a path to balance diversity and feasibility.² Without properly accounting for sparsity and proximity as mentioned above, diversity by itself may not always be useful. The deception that is being forced on people is so counterfactual that it is difficult to avoid the question whether a very dark agenda is in play. machine-learning deep-learning pytorch interpretability explainable-ai xai interpretable-machine-learning explainability counterfactual-explanations nbdev recourse. 2019-04-21 02:20 AM. How Can You Be Content With a Counterfactual Explanation of an Orchestrated "Pandemic"? Their suggestion is that the explanatory role of mathematics can best be captured counterfactually. In general, counterfactuals closer to an individual's profile will be more feasible. 17 papers with code • 0 benchmarks • 1 datasets. Paul Craig Roberts has had careers in scholarship and academia, journalism, public service, and business. COUNTERFACTUALS Counterfactual explanations take a similar form to the statement: You were denied a loan because your annual income Plan for Experiment 2. Challenges. Tags: Coronavirus pharmaceuticals. al n. American Heritage® Dictionary of the English Language, Fifth . Our proposed framework draws motivation from the field of . Counterfactuals have played an equally central role in metaphysics and the philosophy of science. Counterfactual Explanation. The explanation is causal because such a pattern of counterfactual dependence necessarily accompanies a causal relation. Counterfactual reasoning means thinking about alternative possibilities for past or future events: what might happen/ have happened if…? Users can directly inter-pret the perturbation as an "action" for obtaining their desired decision results. The NICE algorithm uses one of the following three properties of a counterfactual explanation for optimum results: Sparsity - It is the count of features required to be modified to achieve the desired outcome. Alternative estimation techniques are first introduced using both the potential outcome model and causal graphs; after which . This is the official repository of the paper "CounterNet: End-to-End Training of Counterfactual Aware Predictions". properties of counterfactual explanation algorithms and comprehensively evaluate all currently-proposed algorithms against that rubric. conclude that unconditional counterfactual explanations can bridge the gap between the interests of data subjects and data controllers that otherwise acts as a barrier to a legally binding right to explanation. There are both model-agnostic and model-specific counterfactual explanation methods, but in this chapter we focus on model-agnostic methods that only work with the model inputs and outputs (and not . It contains commands to estimate and make inference on quantile e ects constructed from counterfactual distributions. Most explanation techniques, however, face an inherent tradeoff between fidelity and interpretability: a high-fidelity explanation for an ML model tends to be complex and hard to interpret, while an interpretable explanation is often inconsistent with the ML model it was meant to explain. In this paper, we develop a set-theoretic and possible worlds approach to counterfactual analysis in case-study explanation. A counterfactual explanation for instance \({\mathbf {x}}\) 's classification is a set of active features with indices \(E \subseteq I_{A}\) such that removing all features with indices E from the instance \({\mathbf {x}}\) leads C to produce another classification. A counterfactual explanation of a prediction describes the smallest change to the feature values that changes the prediction to a predefined output. Reference from: knivesland.com,Reference from: www.macht-geld-sinn.de,Reference from: techurity.net,Reference from: ykravmaga.springweb.co,
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