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.