# ADR-019: D-Separation as Primary Falsification Tool

| Field     | Value                                    |
|-----------|------------------------------------------|
| Status    | Accepted                                 |
| Type      | 1 — Architectural                        |
| Score     | 0.88                                     |
| Date      | 2026-02-18                               |
| Decider   | Conductor                                |

## Context

If a DAG implies X is independent of Y given Z, this must hold in data.
Violations are direct evidence of model misspecification — the strongest
testable implication of any causal model. This applies equally to
expert-built DAGs (credit ontology) and algorithmically discovered DAGs.

## Decision

Use d-separation testing as the primary falsification mechanism. For every
conditional independence implied by the DAG, test against data. Report
violation rate and worst offenders. Complement with placebo and subset
refutation tests from the causal inference literature.

## Consequences

- **Strongest falsification**: d-separation violations are definitive evidence.
- **Works on any DAG**: expert-built, discovered, or post-intervention.
- **Combinatorial explosion**: limit to low-order conditioning sets (|Z| ≤ 3).
- **Refutation suite**: placebo + subset tests provide additional robustness.
