DECISION: [ADR-009] Causal PD Model Over Logistic Regression
DATE: 2026-02-18
TYPE: 1 (irreversible)
STATUS: Accepted
SOLITON SCORE: 0.82

## Context

Credit risk PD (probability of default) models traditionally use logistic
regression or gradient boosted trees. These models capture correlation but
cannot answer interventional questions like "what if we tighten underwriting?"
because they conflate observational and causal relationships.

SR 11-7 requires models to capture "relevant relationships" — causal models
explicitly represent mechanism, not just correlation.

## Options Considered

### Option A: Logistic Regression (traditional)
Pros: Simple, well-understood, regulatory precedent.
Cons: Cannot answer "what if" questions. Confounds correlation with causation.
Feature importance reflects correlation, not causal contribution.

### Option B: Causal PD using WorldDAG + backdoor adjustment (selected)
Pros: Interventional queries via do-calculus. Causal feature importance.
Explicit confounding control. Aligns with SR 11-7 model governance.
Cons: Requires causal graph specification (domain expertise).
More complex than logistic regression.

### Option C: XGBoost with SHAP
Pros: High predictive accuracy. SHAP provides some interpretability.
Cons: SHAP values are not causal. Cannot distinguish correlation from
causation. "What if" answers are associational, not interventional.

## Decision

Causal PD model using WorldDAG structure, backdoor adjustment for
confounding control, and interventional queries for scenario analysis.
Falls back to observational prediction when causal assumptions are unmet.

## Rationale

The value of Orxaq is causal inference, not prediction accuracy. A bank
can get prediction from any vendor. Only causal models can answer "what
happens if we DO X" vs "what correlates with X." This is the differentiator.

## Consequences

**Easier:** Interventional scenario analysis, regulatory model documentation,
distinguishing correlation from causation in model governance.
**Harder:** Requires domain experts to specify causal graph. More complex
model validation (must validate graph structure, not just predictions).
