DECISION: [ADR-013] Fair Lending via Causal Path Analysis
DATE: 2026-02-18
TYPE: 1 (irreversible)
STATUS: Accepted
SOLITON SCORE: 0.80

## Context

Fair lending regulations (ECOA, CFPB) require that lending decisions
do not discriminate based on protected characteristics (age, race, sex).
Traditional approaches use statistical parity or disparate impact ratios,
which cannot distinguish legitimate business factors from discrimination.

Example: age correlates with default rate. But is the effect direct
(age → default, potentially discriminatory) or indirect through
employment length (age → employment_length → default, legitimate)?

## Options Considered

### Option A: Statistical parity testing
Pros: Simple, widely used, regulatory precedent.
Cons: Cannot distinguish direct discrimination from indirect effects
through legitimate factors. Flags legitimate business practices as
discriminatory. Misses subtle discrimination through proxy variables.

### Option B: Causal path decomposition (selected)
Pearl/Kilbertus framework: decompose total effect of protected
characteristic into direct effect (potentially discriminatory) and
indirect effects through legitimate mediators.
Pros: Distinguishes legitimate vs concerning causal pathways.
Identifies proxy discrimination. Enables counterfactual fairness testing.
Cons: Requires causal graph specification. More complex than parity tests.

### Option C: Algorithmic fairness constraints (in-processing)
Pros: Enforces fairness during model training.
Cons: Treats fairness as optimization constraint, not causal understanding.
Cannot explain WHY a decision is fair or unfair.

## Decision

Causal path decomposition using WorldDAG to separate direct effects of
protected characteristics (concerning) from indirect effects through
legitimate business factors (acceptable).

## Rationale

A bank needs to explain to regulators not just WHETHER disparate impact
exists, but WHY. Causal decomposition provides this: "age affects default
through employment tenure (legitimate) with negligible direct effect
(no discrimination)." No other method provides this level of explanation.

## Consequences

**Easier:** Regulatory explanation, identifying proxy discrimination,
counterfactual "what if" fairness testing.
**Harder:** Must specify which mediators are legitimate business factors
(domain judgment). Causal graph must be defensible.
