Add Counterfactual GL Explorer demo + vynfi-counterfactual-gl dataset

README.md

@@ -23,6 +23,7 @@ train, benchmark, and stress-test audit, fraud-detection, and graph-ML systems.
 | Space | What it does |
 |---|---|
 | 🔍 [**Inverse-Audit Detector**](https://huggingface.co/spaces/VynFi/inverse-audit-demo) | Label-free anomaly detection on a synthetic GL — fit the *normal-system manifold*, then flag journal entries by deviation via two fit-on-self residual arms (per-JE **density** + **relational** account-flow-graph) routed into one risk score. Pick a fraud scenario, see per-arm ROC, recall @ audit budget, and the top suspicious entries. |
+| 🔀 [**Counterfactual GL Explorer**](https://huggingface.co/spaces/VynFi/counterfactual-gl-explorer) | Seed-locked **baseline vs counterfactual** ledgers from a causal-DAG intervention — pick a scenario (control-stress / SoD breakdown), see the effect-field distribution shift, the intervention trace, and the exact changed lines. Byte-deterministic generation, so the diff is *signal, not noise*. |
 | 🛡️ [**Fraud-GNN Demo**](https://huggingface.co/spaces/VynFi/fraud-gnn-demo) | Graph-neural-network fraud detection on the JE network — edge fraud predictor, node anomaly explorer, and a live check with confusion matrix + ROC. |
 | 🔗 [**Accounting Network Explorer**](https://huggingface.co/spaces/VynFi/accounting-network-explorer) | Interactive ISO 21378 account-class flow graph — filter by business process, fraud, anomaly, amount, top-N; drill from Level-2 classes into Level-3 sub-classes. |
 | 📊 [**Process Mining Demo**](https://huggingface.co/spaces/VynFi/process-mining-demo) | pm4py directly-follows graphs, variants, and statistics on the supply-chain OCEL 2.0 event log. |
@@ -45,6 +46,11 @@ train, benchmark, and stress-test audit, fraud-detection, and graph-ML systems.
 | [vynfi-journal-entries-10m](https://huggingface.co/datasets/VynFi/vynfi-journal-entries-10m) | Research-scale ~10.9 M-entry synthetic GL. |
 | [vynfi-audit-p2p](https://huggingface.co/datasets/VynFi/vynfi-audit-p2p) | Procure-to-Pay document chain (PO/GR/VI/Payment) with fraud labels — audit-engagement grade. |
 
+**Causal / counterfactual**
+| Dataset | Highlights |
+|---|---|
+| [vynfi-counterfactual-gl](https://huggingface.co/datasets/VynFi/vynfi-counterfactual-gl) | Seed-locked, **byte-deterministic** baseline ↔ counterfactual GL pairs under named causal-DAG interventions (control-environment, SoD) — each pair differs *only* by the intervention's effect; the `diff` split isolates the changed lines. A clean treatment/control substrate for causal ML, treatment-effect estimation, and residual-based audit analytics. |
+
 **AML / banking**
 | Dataset | Highlights |
 |---|---|