• Corpus ID: 246431139

Causal Explanations and XAI

  title={Causal Explanations and XAI},
  author={Sander Beckers},
Although standard Machine Learning models are optimized for making predictions about obser-vations, more and more they are used for making predictions about the results of actions. An important goal of Explainable Artificial Intelligence (XAI) is to compensate for this mismatch by offering explanations about the predictions of an ML-model which ensure that they are reliably action-guiding . As action-guiding explanations are causal explanations, the literature on this topic is starting to… 

Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review

A rubric is designed with desirable properties of counterfactual explanation algorithms and all currently proposed algorithms against that rubric are evaluated, providing easy comparison and comprehension of the advantages and disadvantages of different approaches.

On Causal Rationalization

  • Computer Science
  • 2022
This work formally defines the probability of causation in the rationale model with its identification established as the main component of learning necessary and sufficient rationales.

Backtracking Counterfactuals

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This work focuses on algorithmic recourse, which is concerned with providing explanations and recommendations to individuals who are unfavorably treated by automated decision-making systems, and performs an extensive literature review.



Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers

The problem of feasibility is formulated as preserving causal relationships among input features and a method is presented that uses (partial) structural causal models to generate actionable counterfactuals that better satisfy feasibility constraints than existing methods.

Algorithmic Recourse: from Counterfactual Explanations to Interventions

This work relies on causal reasoning to caution against the use of counterfactual explanations as a recommendable set of actions for recourse, and proposes a shift of paradigm from recourse via nearest counterfactUAL explanations to recourse through minimal interventions, shifting the focus from explanations to interventions.

Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals

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Persuasive Contrastive Explanations for Bayesian Networks

1 Explanation in Artificial Intelligence is often focused on providing reasons for why a model under consideration and its outcome are correct. Recently, research in explainable machine learning has

Actual Causality

  • J. Halpern
  • Philosophy
    A Logical Theory of Causality
  • 2021
Joseph Halpern carefully formulates a definition of causality, and building on this, defines degree of responsibility, degree of blame, and causal explanation, and concludes by discussing how these ideas can be applied to such practical problems as accountability and program verification.

Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice

This article proposes a novel formulation of necessity and sufficiency in XAI, proposes 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.

Causes and Explanations: A Structural-Model Approach. Part II: Explanations

New definitions of (causal) explanation are proposed, using structural equations to model counterfactuals and it is shown that the definition handles well a number of problematic examples from the literature.

Causal Sufficiency and Actual Causation

This paper offers six formal definitions of causal sufficiency and two interpretations of necessity of actual causation, and suggests one definition which comes out as being superior to all others, and is therefore suggested as a new definition ofactual causation.

Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End

An interpretation based on the actual causality framework is provided and how counterfactual examples can be used to evaluate the goodness of an attribution-based explanation in terms of its necessity and sufficiency is shown.

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

LIME is proposed, a novel explanation technique that explains the predictions of any classifier in an interpretable and faithful manner, by learning aninterpretable model locally varound the prediction.