  Machine learning models have undeniably achieved impressive performance
across a range of applications. However, their often perceived black-box
nature, and lack of transparency in decision-making, have raised concerns about
understanding their predictions. To tackle this challenge, researchers have
developed methods to provide explanations for machine learning models. In this
paper, we introduce LaPLACE-explainer, designed to provide probabilistic
cause-and-effect explanations for any classifier operating on tabular data, in
a human-understandable manner. The LaPLACE-Explainer component leverages the
concept of a Markov blanket to establish statistical boundaries between
relevant and non-relevant features automatically. This approach results in the
automatic generation of optimal feature subsets, serving as explanations for
predictions. Importantly, this eliminates the need to predetermine a fixed
number N of top features as explanations, enhancing the flexibility and
adaptability of our methodology. Through the incorporation of conditional
probabilities, our approach offers probabilistic causal explanations and
outperforms LIME and SHAP (well-known model-agnostic explainers) in terms of
local accuracy and consistency of explained features. LaPLACE's soundness,
consistency, local accuracy, and adaptability are rigorously validated across
various classification models. Furthermore, we demonstrate the practical
utility of these explanations via experiments with both simulated and
real-world datasets. This encompasses addressing trust-related issues, such as
evaluating prediction reliability, facilitating model selection, enhancing
trustworthiness, and identifying fairness-related concerns within classifiers.
