Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating

Core Concepts

This study proposes a new explainable artificial intelligence (XAI) framework that combines causal structure information and causal discovery to provide counterfactual explanations for black-box machine learning models, even when the causal graph is unknown.

Abstract

This paper presents a novel XAI framework that relaxes the constraint of requiring the causal graph to be known, which is a limitation of previous methods like LEWIS. The proposed framework leverages counterfactual probabilities and additional prior information on causal structure to integrate a causal graph estimated through causal discovery methods and a black-box classification model. The key highlights of the study are: Analysis of the effects of causal structures on explanation scores and proposal of useful prior information on the causal structure to determine the Nesuf score. Numerical experiments using artificial data to demonstrate the possibility of estimating the global explanatory score and the order of the true feature importance even if the causal graph is not fully known. Application of the proposed method to real-world credit rating data from Shiga Bank, Japan, showing the effectiveness of the approach when the causal graph is unknown. The experiments showed that incorporating prior information on the causal structure, such as the target variable having a direct parent-child relationship with all explanatory variables or being the sink variable, can improve the estimation of the Nesuf score compared to the case where no causal graph is assumed. The results indicate that the proposed framework can provide useful explanations even when the causal graph is unknown, by leveraging causal discovery methods.

Stats

The causal coefficients in the linear causal structures were set to 1 and 1.5 for variables X and Z, respectively, in structure A. The sample size of the artificial data was 5,000. The real-world dataset consisted of 14,018 business customers' credit rating data from Shiga Bank, Japan.

Quotes

"This study proposed a new causal XAI framework that combined causal structure information and causal discovery without the knowledge of the causal graph." "Numerical experiments demonstrated the possibility of estimating the global explanatory score and the order of the true feature importance even if the causal graph was not fully known." "By applying our method to real data, we demonstrated the usefulness of the proposed framework even if the causal graph is unknown."

Key Insights Distilled From

Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating

by Daisuke Taka... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2402.02678.pdf

Deeper Inquiries

What are the potential limitations of the proposed framework, and how could it be further extended to handle more complex causal structures or data types

The proposed framework for explainable artificial intelligence (XAI) using causal discovery with counterfactual explanations has several potential limitations. One limitation is the assumption of linearity in the causal relationships, which may not hold in more complex real-world scenarios where non-linear relationships exist. To address this limitation, the framework could be extended to incorporate non-linear causal models, such as non-linear structural equation models or non-linear additive noise models. By allowing for non-linear relationships, the framework could better capture the complexities of real-world data and provide more accurate explanations. Another limitation is the handling of mixed data types, where both continuous and discrete variables are present. The framework currently focuses on continuous variables, and extending it to handle mixed data types effectively would enhance its applicability to a wider range of datasets. Techniques like mixed data causal discovery algorithms could be integrated to address this limitation and provide comprehensive explanations for mixed data scenarios.

How could the insights from the counterfactual explanations be used to inform decision-making processes in the credit rating domain or other real-world applications

The insights from the counterfactual explanations provided by the framework can be valuable in informing decision-making processes in the credit rating domain and other real-world applications. In the credit rating domain, understanding the factors that influence credit ratings through counterfactual explanations can help financial institutions make more informed lending decisions. For example, if the framework identifies that a specific variable, such as the amount of capital stock, has a significant impact on credit ratings, financial institutions can adjust their lending criteria accordingly. In other real-world applications, such as healthcare or predictive maintenance, the insights from counterfactual explanations can assist in understanding the factors influencing outcomes. For instance, in healthcare, identifying the key variables that impact patient outcomes can aid healthcare providers in personalized treatment plans. By leveraging the insights from counterfactual explanations, decision-makers can make more informed and data-driven decisions in various domains.

What other types of prior information on the causal structure could be leveraged to improve the performance of the proposed XAI framework, and how could these be incorporated systematically

To improve the performance of the proposed XAI framework, various types of prior information on the causal structure can be leveraged systematically. One type of prior information that could be beneficial is the inclusion of domain knowledge constraints. By incorporating domain-specific constraints into the causal discovery process, such as known causal relationships or constraints on variable interactions, the framework can generate more accurate causal graphs and explanations. Additionally, leveraging temporal information could enhance the framework's performance. By considering the temporal order of variables and their causal relationships over time, the framework can capture dynamic causal dependencies and provide more insightful explanations. Techniques like dynamic Bayesian networks or time-series causal discovery methods could be integrated to incorporate temporal information effectively. Moreover, incorporating uncertainty measures into the causal discovery process can improve the robustness of the framework. By quantifying the uncertainty in causal relationships and counterfactual explanations, decision-makers can have more confidence in the insights provided by the framework. Techniques like probabilistic graphical models or Bayesian causal inference can be utilized to incorporate uncertainty measures systematically.