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