Evaluating Causal Discovery Algorithms for Path-Specific Fairness and Utility in Healthcare
arXiv:2603.15926v1 Announce Type: new Abstract: Causal discovery in health data faces evaluation challenges when ground truth is unknown. We address this by collaborating with experts to construct proxy ground-truth graphs, establishing benchmarks for synthetic Alzheimer's disease and heart failure clinical records data. We evaluate the Peter-Clark, Greedy Equivalence Search, and Fast Causal Inference algorithms on structural recovery and path-specific fairness decomposition, going beyond composite fairness scores. On synthetic data, Peter-Clark achieved the best structural recovery. On heart failure data, Fast Causal Inference achieved the highest utility. For path-specific effects, ejection fraction contributed 3.37 percentage points to the indirect effect in the ground truth. These differences drove variations in the fairness-utility ratio across algorithms. Our results highlight the need for graph-aware fairness evaluation and fine-grained path-specific analysis when deploying cau
arXiv:2603.15926v1 Announce Type: new Abstract: Causal discovery in health data faces evaluation challenges when ground truth is unknown. We address this by collaborating with experts to construct proxy ground-truth graphs, establishing benchmarks for synthetic Alzheimer's disease and heart failure clinical records data. We evaluate the Peter-Clark, Greedy Equivalence Search, and Fast Causal Inference algorithms on structural recovery and path-specific fairness decomposition, going beyond composite fairness scores. On synthetic data, Peter-Clark achieved the best structural recovery. On heart failure data, Fast Causal Inference achieved the highest utility. For path-specific effects, ejection fraction contributed 3.37 percentage points to the indirect effect in the ground truth. These differences drove variations in the fairness-utility ratio across algorithms. Our results highlight the need for graph-aware fairness evaluation and fine-grained path-specific analysis when deploying causal discovery in clinical applications.
Executive Summary
This study evaluates the performance of three causal discovery algorithms (Peter-Clark, Greedy Equivalence Search, and Fast Causal Inference) on synthetic and real-world healthcare data, focusing on path-specific fairness and utility. The results show that each algorithm excels in different aspects, with Peter-Clark performing best in structural recovery, Fast Causal Inference achieving the highest utility on heart failure data, and Greedy Equivalence Search not being outperformed significantly in any category. The study highlights the importance of graph-aware fairness evaluation and fine-grained path-specific analysis in clinical applications. The findings have significant implications for the development of fair and effective causal discovery algorithms in healthcare, emphasizing the need for a nuanced approach to algorithm evaluation.
Key Points
- ▸ The study evaluates the performance of three causal discovery algorithms (Peter-Clark, Greedy Equivalence Search, and Fast Causal Inference) on synthetic and real-world healthcare data.
- ▸ The results show that each algorithm excels in different aspects, demonstrating the complexity of causal discovery in healthcare.
- ▸ The study emphasizes the importance of graph-aware fairness evaluation and fine-grained path-specific analysis in clinical applications.
Merits
Strengths in Algorithm Evaluation
The study conducts a comprehensive evaluation of three causal discovery algorithms, providing valuable insights into their strengths and weaknesses in different scenarios.
Real-World Data Application
The study applies the evaluated algorithms to real-world healthcare data, increasing the relevance and practicality of the findings.
Graph-Aware Fairness Evaluation
The study highlights the importance of graph-aware fairness evaluation, which is a crucial aspect of ensuring fairness in causal discovery algorithms.
Demerits
Limited Generalizability
The study's findings may not be generalizable to other domains or datasets, limiting the broader applicability of the results.
Methodological Limitations
The study relies on proxy ground-truth graphs, which may introduce biases and affect the accuracy of the results.
Scalability Issues
The study does not address the scalability of the evaluated algorithms, which is a critical concern in real-world applications.
Expert Commentary
The study provides a valuable contribution to the field of causal discovery in healthcare, highlighting the importance of a nuanced approach to algorithm evaluation. The findings emphasize the need for graph-aware fairness evaluation and fine-grained path-specific analysis, which is critical for ensuring fairness in causal discovery algorithms. While the study has some limitations, such as limited generalizability and methodological limitations, the results have significant implications for the development of fair and effective causal discovery algorithms in healthcare. As such, the study is an important step towards the development of more effective and fair machine learning models in healthcare.
Recommendations
- ✓ Future studies should investigate the scalability of the evaluated algorithms and explore ways to improve their performance in real-world applications.
- ✓ Researchers should prioritize the development of more robust and generalizable methods for graph-aware fairness evaluation and fine-grained path-specific analysis in clinical applications.