DECISION: [ADR-012] Synthetic Ground Truth for Model Validation
DATE: 2026-02-18
TYPE: 2 (reversible)
STATUS: Accepted
SOLITON SCORE: 0.88

## Context

Causal models require validation that goes beyond predictive accuracy.
We need to verify that the model recovers known causal effects, not just
correlations. Real data does not come with ground truth causal effects.

SR 11-7 requires "effective challenge" — the strongest form of model
validation is testing against data where the true causal effects are known.

## Options Considered

### Option A: Validate on real data only
Pros: Tests real-world performance.
Cons: No ground truth for causal effects. Cannot verify if model
correctly distinguishes cause from correlation.

### Option B: Synthetic data with known causal structure (selected)
Pros: Ground truth causal effects are known by construction.
Can verify interventional predictions exactly. Reproducible with seed.
Cons: Synthetic data may not capture all real-world complexity.

### Option C: Semi-synthetic (real marginals, synthetic structure)
Pros: More realistic than fully synthetic.
Cons: Complex to implement, marginals may leak real causal info.

## Decision

Build a synthetic data generator that takes a WorldDAG + structural
equations and produces data with known causal effects. This is the
primary validation tool for the causal PD model.

## Rationale

SR 11-7's "effective challenge" principle requires independent validation.
Synthetic ground truth is the strongest possible challenge for a causal
model: we know the true data-generating process and can verify the model
recovers it. No other validation method provides this guarantee.

## Consequences

**Easier:** Model validation, regression testing, unit testing.
**Harder:** Must maintain structural equations alongside the DAG.
Synthetic data is necessary but not sufficient — real data validation
still required for production deployment.
