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.