A Framework for Generating Feasible and Sparse Counterfactual Explanations Incorporating Causal Constraints

Keskeiset käsitteet

This work presents a framework for generating feasible and sparse counterfactual explanations that satisfy causal constraints, enabling the production of actionable insights for real-world applications.

Tiivistelmä

The paper proposes a framework for generating counterfactual explanations that are both feasible and sparse, satisfying causal constraints derived from domain knowledge. The key aspects are:

Feasibility: The generated counterfactual examples must satisfy logical causal constraints, ensuring they are applicable in real-world scenarios. Unary and binary constraints are used to capture relationships between features.

Sparsity: The framework aims to generate counterfactual examples that require the fewest changes to the input features, making them more actionable for users.

Manifold Representation: The latent space of a Variational Autoencoder is used to visualize the density of feasible and infeasible counterfactual examples, allowing identification of regions more likely to produce useful counterfactuals.

The framework is evaluated on three benchmark datasets - Adult, KDD Census-Income, and Law School. It outperforms existing methods in terms of feasibility, validity, and sparsity of the generated counterfactual examples. The manifold representations provide additional insights into the distribution of feasible and infeasible counterfactuals.

Tilastot

The average number of changes required to generate a feasible counterfactual example (sparsity) ranges from 4.33 to 9.39 across the three datasets.
The feasibility score, i.e., the percentage of counterfactual examples satisfying the causal constraints, ranges from 72.38% to 94.10% for the unary constraint model, and from 77.54% to 86.66% for the binary constraint model.
The validity, i.e., the percentage of counterfactual examples with the desired class, is 100% for most of the experiments.

Lainaukset

"The imminent need to interpret the output of a Machine Learning model with counterfactual (CF) explanations – via small perturbations to the input – has been notable in the research community."
"However, there are several questions arisen regarding these examples. Are actually all these scenarios applicable to the real-world? Are all counterfactual explanation easily adaptable to real-world applications? The answer is negative to these questions."

Tärkeimmät oivallukset

A Framework for Feasible Counterfactual Exploration incorporating Causality, Sparsity and Density

by Kleopatra Ma... klo arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.13476.pdf

Syvällisempiä Kysymyksiä

What other types of causal constraints or domain knowledge could be incorporated to further improve the feasibility and usefulness of the generated counterfactual explanations

Incorporating additional types of causal constraints or domain knowledge can significantly enhance the feasibility and usefulness of generated counterfactual explanations. One approach could involve integrating temporal constraints, where the order of events or changes in a system must adhere to a logical sequence. For example, in a loan approval scenario, it would be illogical for someone to pay off a loan before actually receiving it. By incorporating temporal constraints, the counterfactual explanations generated would align more closely with real-world scenarios and be more actionable for users.
Another type of constraint that could be beneficial is contextual constraints. These constraints consider the broader context in which the decision is made. For instance, in a healthcare setting, the availability of certain medical resources or the patient's medical history could influence the feasibility of a suggested counterfactual action. By incorporating contextual constraints, the generated explanations would be more tailored to the specific circumstances of the individual, increasing their relevance and practicality.

How could this framework be extended to handle more complex datasets or tasks beyond binary classification

To extend this framework to handle more complex datasets or tasks beyond binary classification, several modifications and enhancements can be implemented. One approach is to incorporate multi-class classification, where the model predicts among multiple classes instead of just two. This expansion would require adjusting the classifier and the counterfactual generation process to accommodate the additional classes.
Furthermore, for more complex datasets with a higher number of features, dimensionality reduction techniques such as PCA or t-SNE can be employed to reduce the complexity of the data while preserving important information. This would help in visualizing and interpreting the data more effectively.
Additionally, for tasks beyond classification, such as regression or clustering, the framework can be adapted by modifying the loss functions and evaluation metrics to suit the specific task requirements. For regression tasks, the feasibility of counterfactual explanations can be evaluated based on the proximity of predicted numerical values, while for clustering tasks, the explanations can focus on grouping similar instances together based on certain criteria.

Can the manifold representation be leveraged to provide additional insights or guidance to users in interpreting the counterfactual explanations

The manifold representation derived from the latent space of the Variational Autoencoder (VAE) can offer valuable insights and guidance to users in interpreting the counterfactual explanations. By visualizing the feasible and infeasible counterfactual examples on the manifold, users can gain a better understanding of the distribution and density of the generated explanations.
The manifold representation can help users identify clusters or regions where feasible counterfactual examples are concentrated, providing a visual guide for selecting the most relevant and practical explanations. Users can navigate the manifold to explore different regions and understand the relationships between different counterfactual examples.
Moreover, the manifold can be leveraged to identify outliers or unusual counterfactual examples that may not align with the majority of feasible explanations. This can help users in filtering out unrealistic or impractical suggestions, ensuring that the generated counterfactual explanations are not only feasible but also relevant to the specific context of the problem.