# 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
```typescript
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