Explaining Causal Models with Argumentation: the Case of Bi-variate Reinforcement

• URL: http://arxiv.org/abs/2205.11589v1
• Date: Mon, 23 May 2022 19:39:51 GMT
• Title: Explaining Causal Models with Argumentation: the Case of Bi-variate Reinforcement
• Authors: Antonio Rago, Pietro Baroni and Francesca Toni
• Abstract summary: We introduce a conceptualisation for generating argumentation frameworks (AFs) from causal models. The conceptualisation is based on reinterpreting desirable properties of semantics of AFs as explanation moulds. We perform a theoretical evaluation of these argumentative explanations, examining whether they satisfy a range of desirable explanatory and argumentative properties.
• Score: 15.947501347927687
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Causal models are playing an increasingly important role in machine learning, particularly in the realm of explainable AI. We introduce a conceptualisation for generating argumentation frameworks (AFs) from causal models for the purpose of forging explanations for the models' outputs. The conceptualisation is based on reinterpreting desirable properties of semantics of AFs as explanation moulds, which are means for characterising the relations in the causal model argumentatively. We demonstrate our methodology by reinterpreting the property of bi-variate reinforcement as an explanation mould to forge bipolar AFs as explanations for the outputs of causal models. We perform a theoretical evaluation of these argumentative explanations, examining whether they satisfy a range of desirable explanatory and argumentative properties.

Related papers

• Counterfactual explainability of black-box prediction models [4.14360329494344]
  We propose a new notion called counterfactual explainability for black-box prediction models. Counterfactual explainability has three key advantages.
  arXiv  Detail & Related papers  (2024-11-03T16:29:09Z)
• Hard to Explain: On the Computational Hardness of In-Distribution Model Interpretation [0.9558392439655016]
  The ability to interpret Machine Learning (ML) models is becoming increasingly essential. Recent work has demonstrated that it is possible to formally assess interpretability by studying the computational complexity of explaining the decisions of various models.
  arXiv  Detail & Related papers  (2024-08-07T17:20:52Z)
• Explainability for Large Language Models: A Survey [59.67574757137078]
  Large language models (LLMs) have demonstrated impressive capabilities in natural language processing. This paper introduces a taxonomy of explainability techniques and provides a structured overview of methods for explaining Transformer-based language models.
  arXiv  Detail & Related papers  (2023-09-02T22:14:26Z)
• Logical Satisfiability of Counterfactuals for Faithful Explanations in NLI [60.142926537264714]
  We introduce the methodology of Faithfulness-through-Counterfactuals. It generates a counterfactual hypothesis based on the logical predicates expressed in the explanation. It then evaluates if the model's prediction on the counterfactual is consistent with that expressed logic.
  arXiv  Detail & Related papers  (2022-05-25T03:40:59Z)
• Argumentative Explanations for Pattern-Based Text Classifiers [15.81939090849456]
  We focus on explanations for a specific interpretable model, namely pattern-based logistic regression (PLR) for binary text classification. We propose AXPLR, a novel explanation method using (forms of) computational argumentation to generate explanations.
  arXiv  Detail & Related papers  (2022-05-22T21:16:49Z)
• Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Models [77.34726150561087]
  We define explainability through the interpretability of the explanations and the faithfulness of the explainability model in the field of process outcome prediction. This paper contributes a set of guidelines named X-MOP which allows selecting the appropriate model based on the event log specifications.
  arXiv  Detail & Related papers  (2022-03-30T05:59:50Z)
• Explanatory Pluralism in Explainable AI [0.0]
  I chart a taxonomy of types of explanation and the associated XAI methods that can address them. When we look to expose the inner mechanisms of AI models, we produce Diagnostic-explanations. When we wish to form stable generalizations of our models, we produce Expectation-explanations. Finally, when we want to justify the usage of a model, we produce Role-explanations.
  arXiv  Detail & Related papers  (2021-06-26T09:02:06Z)
• Thinking About Causation: A Causal Language with Epistemic Operators [58.720142291102135]
  We extend the notion of a causal model with a representation of the state of an agent. On the side of the object language, we add operators to express knowledge and the act of observing new information. We provide a sound and complete axiomatization of the logic, and discuss the relation of this framework to causal team semantics.
  arXiv  Detail & Related papers  (2020-10-30T12:16:45Z)
• The Struggles of Feature-Based Explanations: Shapley Values vs. Minimal Sufficient Subsets [61.66584140190247]
  We show that feature-based explanations pose problems even for explaining trivial models. We show that two popular classes of explainers, Shapley explainers and minimal sufficient subsets explainers, target fundamentally different types of ground-truth explanations.
  arXiv  Detail & Related papers  (2020-09-23T09:45:23Z)
• CausalVAE: Structured Causal Disentanglement in Variational Autoencoder [52.139696854386976]
  The framework of variational autoencoder (VAE) is commonly used to disentangle independent factors from observations. We propose a new VAE based framework named CausalVAE, which includes a Causal Layer to transform independent factors into causal endogenous ones. Results show that the causal representations learned by CausalVAE are semantically interpretable, and their causal relationship as a Directed Acyclic Graph (DAG) is identified with good accuracy.
  arXiv  Detail & Related papers  (2020-04-18T20:09:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.