External pipelines · RISE project catalogue
RECAST (Replication and Extension with Causal AI Statistical Toolkit)
An end-to-end autonomous pipeline for the *replication + extension + peer-review* arc of the RISE concept diagram. Given a published econometrics paper and its replication data, RECAST reproduces the original results, extends them using Double/Debiased Machine Learning (7 ML methods, 20+ sample splits) and Causal Forests, runs the extended findings through a structured three-referee AI review, and emits a final synthesis report.
Where it sits
left: what it builds on · right: what builds on it · pale: exampleContributed by Quentin Gallea
How studies reach it
No published study reaches it yet.
Disciplines it reaches
No study reaches it yet.
Solid: published studies. Light: examples.
Computed from the records on this site: what each study, template and specialist names as used, which study extends which, and who contributed what. 0 studies in total.
What it does
Couples *replication* with *methodological extension* — most agentic-replication tools stop at "did we get the same numbers?"; RECAST proposes that the same agentic infrastructure should also push existing papers forward with modern causal-ML estimators and have its own output stress-tested by AI referees before publication.
- Focus
- replication
- Inputs
- target-paper, target-dataset
- Outputs
- replication-report, extension-results, referee-reports, synthesis-report, code
- Architecture
- tool-use, multi-agent-review, dag-orchestration, artifact-versioning, claude-code-skill-files
- Maintained by
- Quentin Gallea (thecausalmindset.com)
- Started
- 2026
Description
Data model- Discipline
- Economics
- Method family
- Replication
- Design
- not specified
- Research stage
- ReplicationData acquisitionData analysisCode generationReviewDrafting
- Contributors
- Quentin Gallea
- Usage
- not used in published research yet
- Source
- RISE project catalogue · projects/landscape · @4c17bae
- Record
- pipeline:recast-causal-ai · JSON
Solid tags are declared by the source or mapped from its terms; dashed tags are inferred by a published rule. Hover a tag for its provenance.
Bring it into the standard
A pipeline built outside E2ER can meet the standard by describing its steps as a template, attaching the floor of checks and publishing evaluation records. Its authors keep ownership and credit.