An Identifiable Cost-Aware Causal Decision-Making Framework Using Counterfactual Reasoning

TL;DR

This paper introduces MiCCD, a cost-aware causal decision-making framework using counterfactual reasoning, which effectively identifies optimal interventions in abnormal system states while minimizing costs.

Contribution

It develops an identifiable causal decision framework with a surrogate model and counterfactual optimization, addressing limitations of existing methods in cost and causal mechanism integration.

Findings

MiCCD outperforms traditional methods in F1-score and cost efficiency.

The framework effectively handles mixed anomaly data.

Experimental results validate broad applicability and improved decision quality.

Abstract

Decision making under abnormal conditions is a critical process that involves evaluating the current state and determining the optimal action to restore the system to a normal state at an acceptable cost. However, in such scenarios, existing decision-making frameworks highly rely on reinforcement learning or root cause analysis, resulting in them frequently neglecting the cost of the actions or failing to incorporate causal mechanisms adequately. By relaxing the existing causal decision framework to solve the necessary cause, we propose a minimum-cost causal decision (MiCCD) framework via counterfactual reasoning to address the above challenges. Emphasis is placed on making counterfactual reasoning processes identifiable in the presence of a large amount of mixed anomaly data, as well as finding the optimal intervention state in a continuous decision space. Specifically, it formulates a surrogate model based on causal graphs, using abnormal pattern clustering labels as supervisory signals. This enables the approximation of the structural causal model among the variables and lays a foundation for identifiable counterfactual reasoning. With the causal structure approximated, we then established an optimization model based on counterfactual estimation. The Sequential Least Squares Programming (SLSQP) algorithm is further employed to optimize intervention strategies while taking costs into account. Experimental evaluations on both synthetic and real-world datasets reveal that MiCCD outperforms conventional methods across multiple metrics, including F1-score, cost efficiency, and ranking quality(nDCG@k values), thus validating its efficacy and broad applicability.