The proposed sensitivity analysis (SA)-based framework for interpretable machine learning (IML) can be extended to handle causal relationships and inferences by integrating causal inference methodologies into the existing framework. This can be achieved through several approaches:
Causal Graphs: Incorporating causal graphs or directed acyclic graphs (DAGs) can help in visualizing and understanding the causal relationships between features and outcomes. By defining the causal structure, one can identify which variables are confounders, mediators, or colliders, thus allowing for a more nuanced interpretation of the model outputs.
Counterfactual Analysis: The framework can include counterfactual reasoning, which examines what would happen to the outcome if a feature were altered. This aligns with the concept of causal inference, where one seeks to understand the effect of interventions. Techniques such as potential outcomes and do-calculus can be integrated to assess the impact of changes in input variables on the predicted outcomes.
Causal Sensitivity Analysis: Extending traditional SA methods to evaluate how sensitive causal inferences are to changes in model assumptions or input distributions can provide insights into the robustness of the causal claims made by the model. This involves assessing how variations in feature values influence causal relationships, thereby enhancing the interpretability of the model.
Integration of Machine Learning with Causal Models: By combining machine learning models with causal models, one can leverage the strengths of both approaches. For instance, using causal forests or targeted maximum likelihood estimation (TMLE) can help in estimating causal effects while maintaining the predictive power of machine learning.
Validation of Causal Claims: The framework should include mechanisms for validating causal claims through experimental or quasi-experimental designs, such as randomized controlled trials (RCTs) or natural experiments. This validation can enhance the credibility of the causal inferences drawn from the model.
By incorporating these elements, the SA-based framework for IML can evolve to not only predict outcomes but also provide insights into the underlying causal mechanisms, thereby enriching the interpretability and applicability of machine learning models in real-world scenarios.