counterfactual explanation

Counterfactual Explanation Based on Gradual Construction ... 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. 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 . The generated samples can act as a recommendation for end-users to achieve their desired outputs. Donate. DiCE: Diverse Counterfactual Explanations for Machine ... A comparison of instance-level counterfactual explanation ... We pass the counterfactual feature as a counterfactual explanation to the questioner. Here is a collection of articles from Global Research documenting the Covid Deception that has been fostered on the Western World. Most influentially, Woodward [2003] has portrayed causal scientific explanations as operating by providing information about systematic patterns of counterfactual dependence — about how the explanandum would have been different, had facts in the explanans . How Can You Be Content With a Counterfactual Explanation ... 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. 09/20/2019 ∙ by Rafael Poyiadzi, et al. Counterfactual Explanations vs. Attribution Based Explanations Counterfactual conditionals (also subjunctive or X-marked) are conditional sentences which discuss what would have been true under different circumstances, e.g. Citation Counterfactual conditionals (also subjunctive or X-marked) are conditional sentences which discuss what would have been true under different circumstances, e.g. Counterfactual explanation is a powerful but straightforward method to improve explanability of machine learning models. 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? A natural causal explanation methodology would be to generate counterfactual examples and compare the model prediction for each example with its prediction for the counterfactual. While this sounds simple, there are several challenges in setting up and solving this optimization problem. PDF Counterfactual Explanations of Machine Learning ... 9.3 Counterfactual Explanations | Interpretable Machine ... Mathematical counterfactuals with number-theoretic ... This paper is a note on new directions and methodologies for validation and explanation of Machine Learning (ML) models employed for retail credit scoring in finance. In this paper, we develop a set-theoretic and possible worlds approach to counterfactual analysis in case-study explanation. Counterfactual Instances — Alibi 0.6.2 documentation 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, without needing to explain . Our proposed framework draws motivation from the field of . ∙ Ghent University ∙ University of Bristol ∙ 14 ∙ share. Reutlinger (Eur J Philos Sci 7(2):239-256, 2017 . The official Covid narrative is a total lie. Abstract. It contains commands to estimate and make inference on quantile e ects constructed from counterfactual distributions. Explanations are Selected Explanations provided are a subset from a possibly infinite set of explanations, based on a certain set of cognitive biases. The past two decades have witnessed an explosion of interest in counterfactual accounts of causal scientific explanation. In the area of explainable AI, counterfactual explanation would be contrastive in nature and would be better received by the human receiving the explanation. In the folder "01_KNIME_Workflows" you can find the example workflows to explain predictions in KNIME from Keras and . 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 . Data. Paul Craig Roberts. Hashemi, Masoud. Challenges. Their suggestion is that the explanatory role of mathematics can best be captured counterfactually. How Can You Be Content With a Counterfactual Explanation of an Orchestrated "Pandemic"? Request PDF | A Counterfactual Explanation for the Action Effect in Causal Judgment | People's causal judgments are susceptible to the action effect, whereby they judge actions to be more causal . To Returns a contrastive argument that permits to achieve the desired class, e.g., "to obtain this loan, you need XXX of annual revenue instead of the current YYY". By contrast, the . This paper focuses on their example with a number-theoretic antecedent. PermuteAttack: Counterfactual Explanation of Machine Learning Credit Scorecards. Proximity - It refers to the difference between the actual input and the counterfactual instance. _READ ME.pdf. The explanation is causal because such a pattern of counterfactual dependence necessarily accompanies a causal relation. The official Covid narrative is a total lie. Action Rules: Counterfactual Explanations in Python? 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. Deep neural networks (DNNs) can accurately decode task-related information from brain activations. Both look for minimal changes, although the latter looks for a more constrained change (additions), to the input for the decision of the . Feasibility of counterfactual explanations. 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. 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. However, an action 1. 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 ). Plan for Experiment Sb. Paul Craig Roberts. The counterfactual explanation that explains why document 01's predicted topic is Medical is shown in Figure 2b. Using this approach, we first consider four kinds of counterfactuals: necessary condition counterfactuals, SUIN condition counterfactuals, sufficient condition counterfactuals, and INUS condition counterfactuals. What is counterfactual thinking? Counterfactual explanations (CE) provide means for prescriptive model explanations by suggesting actionable feature changes to allow individuals to achieve favorable outcomes in the future. INTRODUCTION. ; Fathi, Ali. Counterfactual Explanation: Given an input data that are classified as a class from a deep network, it is to perturb the subset of features in the input data such that the model is forced to predict the perturbed data as a target class. 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. Dear Readers, Here is a collection of articles from Global Research documenting the Covid Deception that has been fostered on the Western World. Our focus, in particular, is on intra-mathematical explanations: explanations of one mathematical fact in terms of another. Alternatively, contrastive and counterfactual explanations justify why the output of the algorithms is not any different and how it could be changed, respectively. 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. The feature that has the great-est effect on the model prediction result is what we currently believe is the essential counterfactual feature. Finally, we extend our discussion of 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". While this sounds simple, there are several challenges in setting up and solving this optimization problem. 2019-05-09 03:59 PM. COUNTERFACTUALS Counterfactual explanations take a similar form to the statement: You were denied a loan because your annual income Such explanations are more desirable in expert domains. 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 biggest shortcoming of many machine learning models and neural networks is their "blackbox" nature. How Can You Be Content With a Counterfactual Explanation of an Orchestrated "Pandemic"? title = "A counterfactual approach to explanation in mathematics", abstract = "Our goal in this paper is to extend counterfactual accounts of scientific explanation to mathematics. Action rule mining is an extension of the widely used task of learning classi cation rules. Here is a collection of articles from Global Research documenting the Covid Deception that has been fostered on the Western World. FACE: Feasible and Actionable Counterfactual Explanations. The R package Counterfactual implements the methods of Cher-nozhukov et al. explanation for the state of the component; however, the "had failed" wording suggests an explanation that is local to the component (e.g., the component is internally broken). A counterfactual explanation of a prediction describes the smallest change to the feature values that changes the prediction to a predefined output. In general, counterfactuals closer to an individual's profile will be more feasible. 1. Answer (1 of 2): Hi - I'm one of the authors on several of the CFR papers, including the original 2007 paper and the recent Science paper where we used CFR+ to solve heads-up limit hold'em. The deception that is being forced on people is so counterfactual that it is… 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. plored counterfactual visual explanations from the perspec-tive of counterfactual thinking. (2013) for counterfactual analysis. the biological status might be of the above explanation of the empirical phenomenon, what Baron, Colyvan, and Ripley are concerned with is the embedding of these kind of extra-mathematical explanations in the general framework of a counterfactual theory of explanation. 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. Some of these articles below give one part of the deception but aren't aware of other . Work in Counterfactual Explanations tends to focus on the principle of "the closest possible world" that identifies small changes leading to the desired outcome. Counterfactual Explanation. Troubleshooting, for example, often 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 . From perturbed data, we can interpret that the pre-trained . 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 . A Counterfactual Explanation for the Action Effect. Most counterfactual analyses have focused on claims of the form "event c caused event e", describing 'singular' or 'token' or 'actual' causation. They have featured in metaphysical theories of causation, supervenience, grounding, ontological dependence, and dispositions. They consider the following counterfactual statement (Baron, Colyvan, and . .. In this paper, we develop a set-theoretic and possible worlds approach to counterfactual analysis in case-study explanation. OSF Storage (United States) _Action Effect_preprint.pdf. Given a perturbation vector, the user can interpret it as an action . The official Covid narrative is a total lie. 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 . Our insouciance and inaction are allowing the net of deception to close us in and prevent our resistance. Users can directly inter-pret the perturbation as an "action" for obtaining their desired decision results. In other words, you imagine the consequences of something that is contrary to what actually happened or will have happened ("counter to the facts"). 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. Intro. Using this approach, we first consider four kinds of counterfactuals: necessary condition counterfactuals, SUIN condition counterfactuals, sufficient condition counterfactuals, and INUS condition counterfactuals. Counterfactual thinking is, as it states: "counter to the facts". 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 is one branch of interpretable machine learning that produces a perturbation sample to change the model's original decision. September 23, 2021. Counterfactuals have played an equally central role in metaphysics and the philosophy of science. Updated on Sep 18. Alternative estimation techniques are first introduced using both the potential outcome model and causal graphs; after which . How Can You Be Content With a Counterfactual Explanation of an Orchestrated "Pandemic"? A kind of non-causal explanation that has gained much attention recently but that this theory seems unable to account for are grounding explanations. Definition and explanation. 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). He is chairman of The Institute for Political Economy. 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. The program . 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. It is of crucial importance to bridge the gap between theoretical approaches to contrastive and counterfactual explanation and the corresponding computational frameworks. Counterfactual explanations [41] have been widely adopted to interpret algorithmic decisions in many real world applications [16, 25, 42, 61], due to its beauty of being concise and easy to under-stand [41, 58]. Counterfactual reasoning means thinking about alternative possibilities for past or future events: what might happen/ have happened if…? properties of counterfactual explanation algorithms and comprehensively evaluate all currently-proposed algorithms against that rubric. Thus, we predict that participants in the not operated and failed conditions will make different counterfactual 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]. II. 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. in the conditional distribution of Y given X. Counterfactual analysis consists of evaluating the e ects of such changes. 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. 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. al n. American Heritage® Dictionary of the English Language, Fifth . Obtaining counterfactual explanation involves identifying the closest point to the input at hand that results in a different prediction. 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 . Consider deciding which road to take driving home. analysis and is a useful way for testing cause-and-effect relationships.. 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. This is the official repository of the paper "CounterNet: End-to-End Training of Counterfactual Aware Predictions". 17 papers with code • 0 benchmarks • 1 datasets. How Can You Be Content With a Counterfactual Explanation of an Orchestrated "Pandemic"? Speaker: Nihal Murali Paper Title: Augmentation by Counterfactual Explanation - Fixing an Overconfident Black-Box Abstract: A highly accurate but overconfident model is ill-suited for decision-making pipelines, especially in critical applications such as healthcare or autonomous driving. Utilizing an analogy between counterfactuals and attitude ascriptions, I provide a counterpossibilist‐friendly explanation for the apparent validity of substitution inferences into counterfactuals. Obtaining counterfactual explanation involves identifying the closest point to the input at hand that results in a different prediction. However, because of the nonlinearity of the DNN, the decisions made by DNNs are hardly interpretable. 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 Explanation (CE) is one of the post-hoc explanation methods that provides a perturba-tion vector so as to alter the prediction result ob-tained from a classiﬁer. Choosing an appropriate method is a crucial aspect of meaningful counterfactual explanation. One of the promising approaches for explaining such a black-box system is counterfactual explanation. On the counterfactual explanation, it is the tendency to consider counterfactuals that engenders the difference in causal judgments for actions and inactions. 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. Our rubric provides easy comparison and comprehension of the advantages and disadvantages of different approaches and serves as an introduction to major research themes in this ﬁeld. We visualize explanations through a table highlighting the change in features. Paul Craig Roberts has had careers in scholarship and academia, journalism, public service, and business. Most counterfactual explanation methods only operate on single input data. Plan for Experiment Sa. 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 . Raw Data. How Can You Be Content With a Counterfactual Explanation of an Orchestrated "Pandemic"? 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. Counterfactual explanations offer a promising alternative. Counterfactual Explanation. PaulCraigRoberts.org. "If Peter believed in ghosts, he would be afraid to be here." Counterfactuals are contrasted with indicatives, which are generally restricted to discussing open possibilities.Counterfactuals are characterized grammatically by their use . Analysis Code. machine-learning deep-learning pytorch interpretability explainable-ai xai interpretable-machine-learning explainability counterfactual-explanations nbdev recourse. The classification outcome should reflect a high uncertainty on ambiguous in-distribution samples that lie . By Paul Craig Roberts. "If Peter believed in ghosts, he would be afraid to be here." Counterfactuals are contrasted with indicatives, which are generally restricted to discussing open possibilities.Counterfactuals are characterized grammatically by their use . Keywords: Counterfactual conditionals; Bayes nets; Explanation; Reasoning 1. explanation of a singular fact works by conveying information about Y's systematic counterfactual dependence, that is, about the answers to certain what-if-things-had-been-different questions ("w-questions"). Plan for Experiment 2. 2019-04-21 02:20 AM. When faced with a prediction of lesion A, a doctor would natu-rally ask "why A but not B?" The same question would be posed by a student that incorrectly assigned an image to 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?". This is posted as a reference to a lot of good articles. 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. Abstract. In this framework, the behavior of a black-box system is explained by comparing real data and . We plan to support an English language explanation too! A counterfactual explanation of a decision is the smallest set of relevant factors such that changing can alter CFR is a self-play algorithm: it learns to play a game by repeatedly playing against itself. Dear Readers, Here is a collection of articles from Global Research documenting the Covid Deception that has been fostered on the Western World. You take Right Ave and encounter lots of traffic. The deception that is being forced on people . Tags: Coronavirus pharmaceuticals. There can be cases where the number of features that need to be changed may be too numerous, or the changes . The Framework for counterfactual explanation is described in Fig 1. Pull requests. Hence, the counterfactual explanation predicts a reverse action effect: in overdetermination cases, inactions should actually be regarded as more causal than actions. A proposal by Baron, Colyvan, and Ripley to extend the counterfactual theory of explanation to include counterfactual reasoning about mathematical explanations of physical facts is discussed. counterfactual or contrastive explanation [36, 8, 24]. 1.3 Metaphysical Analysis and Scientific Explanation. We offer a basic counterfactual theory of .
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