Causal Inference Framework
- Potential Outcomes Framework: Counterfactual reasoning and treatment effect estimation
- Directed Acyclic Graphs (DAGs): Causal structure identification and confounder selection
Technology & Methodologies
A practical deep dive into the technical frameworks and methods we use to build robust, explainable causal AI for healthcare.
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Propensity Score Methods
- Propensity Score Methods: Matching, stratification, and inverse probability weighting
- Instrumental Variables: Two-stage least squares and weak instrument diagnostics
- Doubly Robust Methods: Combining outcome modeling with propensity score approaches
- Mediation Analysis: Direct and indirect effect decomposition
Hybrid Causal-AI Methods
- Causal Deep Learning: Neural networks with causal structure constraints
- Representation Learning for Confounders: Deep learning approaches to confounder identification
- Causal Discovery with AI: Machine learning methods for causal structure learning
- Treatment Effect Heterogeneity: AI-powered personalized treatment effect estimation
- Causal Reinforcement Learning: Decision-making under causal constraints
Synthetic Data & Budget Optimization
- Realistic Data Generation: Synthetic clinical datasets that preserve real-world statistical properties
- Economic Modelling Integration: Cost-effectiveness frameworks embedded within causal inference models
- Realistic Data Generation: Synthetic clinical datasets that preserve real-world statistical properties
- Economic Modelling Integration: Cost-effectiveness frameworks embedded within causal inference models
Explainable Deep Learning Architecture
- Inherently Interpretable Models: Attention-based neural networks with built-in interpretability
- Layer-wise Relevance Propagation: Backpropagation-based feature attribution
- Integrated Gradients: Path-based attribution methods for deep networks
- Concept Activation Vectors: High-level concept interpretation in neural networks
- Prototype-based Networks: Case-based reasoning in deep learning frameworks
- Uncertainty Quantification: Bayesian deep learning for confidence estimation
Validation & Robustness
- Cross-validation for Causal Models: Specialized validation techniques for causal inference
- Sensitivity Analysis Protocols: Testing robustness to unmeasured confounding
- Benchmark Datasets: Standardized evaluation frameworks
- Reproducibility Standards: Open-source implementation and documentation
