Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review.
How this is built →
1Distinct papers
1Unique collaborators
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5144393301
ORCID evidence
No valid ORCID is stored.
Observed aliases (2)
- Faouzi Boussedra (openalex, provider refresh)
- Faouzi Boussedra (openalex, source metadata)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Innovation: 1 paper
Claim outcomes
- Decision Quality: 1 paper
- Output Quality: 1 paper
Papers in the OpenAlex view
Latest stored OpenAlex author observations only. Citation counts below are from the same provider and are not combined with other services.
Scroll the table horizontally to see every column.
| Paper | Author evidence | Date | Provider citations |
|---|---|---|---|
| Regime‑aware causal machine learning markedly improves US macro forecasts: Double Machine Learning gives the most robust out‑of‑sample accuracy, and explainability tools show uncertainty, financial volatility, energy shocks and monetary policy dominate inflation during crises.openalex | Faouzi Boussedra provider id |
2026-08-05 | 0 |
Citation observation summary
OpenAlex supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 0 cumulative citations. This is a coverage summary, not an author score or h-index.