Dynamic Expert-Guided Model Averaging for Causal Discovery
Unlocking Clinical Causal Insights with Expert-Guided AI
A novel model averaging approach leverages dynamic expert knowledge to enhance the robustness and accuracy of causal discovery in healthcare, even with imperfect experts.
Enhancing Trust and Utility in AI-Driven Healthcare Decisions
Causal discovery is pivotal for interpreting predictive models and informing interventions in healthcare. However, the complexity of existing algorithms and inherent data challenges necessitate a robust, adaptive solution. Our method integrates diverse causal discovery algorithms with dynamically requested expert knowledge, offering a significant leap in model reliability and interpretability. This ensures that clinical decisions are informed by the most accurate and contextually relevant causal insights.
Clinical Networks Analyzed
Expert Correctness Threshold
Avg. Performance Improvement (BSF)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Model Averaging Strategy
Our core contribution is a flexible model averaging strategy that uses dynamically requested expert knowledge to mediate discrepancies between models. This novel approach allows for the inclusion of a diverse array of causal discovery algorithms, making it widely applicable across different domains and data types. By treating direct connection existence separately from orientation, and querying experts when model consensus is low, it intelligently refines the causal graph.
Performance Evaluation
We rigorously evaluated our method against several baselines on well-known, real-world graphs, using both clean and noisy data. The results consistently demonstrated broad improvements in BSF and F1 scores, typically by at least 10% over baseline methods. This underscores the method's efficacy in handling the challenges of real-world causal discovery applications, including noisy data and varying sample sizes, providing a more robust ensemble output.
LLMs as Imperfect Experts
A significant aspect of our research explores the utility of Large Language Models (LLMs) as imperfect experts for dynamically answering causal ordering questions. Experiments show that even with 80% correctness, LLMs can effectively mediate model averaging, contributing to improved graphical accuracy. We analyzed the impact of varying expert correctness degrees, providing valuable insights for practitioners on how to leverage AI-driven expert systems in causal discovery workflows.
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Average # of Algorithms in Ensemble
Enterprise Process Flow
Causal Models M
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Connection Counts
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Edge Threshold (θ₁)
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Expert Review (if needed)
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Orientation Threshold (θ₂)
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Expert Orientation (if needed)
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Final Model m
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LLMs for Clinical Causal Ordering
Our experiments utilized OpenAI's gpt-5-nano to dynamically answer causal ordering questions within clinical networks (ASIA, ALARM, SimSUM). Results indicate that LLMs perform better on orientation queries than existence queries, with overall accuracy for ASIA and SimSUM aligning with the 80% correctness used in simulated expert scenarios.
Impact: This demonstrates the viability of integrating LLMs into causal discovery workflows to augment human expertise, particularly for guiding causal direction, even when the LLM's 'knowledge' is imperfect. Challenges were noted for networks like ALARM, potentially due to their diagnostic-centric design rather than physiological causal models.
Advanced ROI Calculator
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Estimated Annual Savings
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Your Implementation Roadmap
A phased approach to integrating expert-guided causal discovery, ensuring seamless adoption and maximum impact.
Phase 1: Initial Assessment & Setup
Evaluate existing causal discovery infrastructure and data sources. Integrate our model averaging framework with your preferred ensemble algorithms.
Phase 2: Expert Integration & Calibration
Configure dynamic expert querying with human or LLM experts. Calibrate confidence thresholds (θ₁, θ₂) based on domain-specific requirements and expert reliability.
Phase 3: Iterative Refinement & Validation
Run initial discovery cycles, gather expert feedback, and refine prompts/expert models. Validate resultant causal graphs against known clinical pathways and outcomes.
Phase 4: Deployment & Continuous Learning
Deploy the expert-guided system for ongoing causal discovery. Establish feedback loops for continuous improvement and adaptation to new data or expert knowledge.
Ready to Elevate Your Causal Discovery?
Partner with us to implement a cutting-edge, expert-guided causal discovery system that delivers robust, interpretable, and actionable insights for your enterprise.
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