1.0 KiB
1.0 KiB
Causal Reasoning Simulation
Overview
Causal relationship analysis with intervention-based reasoning, testing cause-effect hypotheses through graph-based causal edges.
Purpose
Model causal inference using directed acyclic graphs (DAGs) and measure intervention effects (uplift).
Operations
- Causal Pairs: 10-15 cause-effect relationships
- Uplift Measurement: Quantify causal impact
- Confidence Scoring: Bayesian confidence intervals
- Intervention Analysis: Counterfactual reasoning
Results
- Throughput: 3.13 ops/sec
- Latency: 308ms avg
- Causal Edges: 3 per iteration
- Avg Uplift: 10-13%
- Avg Confidence: 92%
Technical Details
await causal.addCausalEdge({
fromMemoryId: causeId,
toMemoryId: effectId,
uplift: 0.12, // 12% improvement
confidence: 0.95,
mechanism: 'implement_caching → reduce_latency'
});
Applications
- A/B testing analysis
- Root cause analysis
- Treatment effect estimation
- Policy evaluation
Status: ✅ Operational