Causal discovery

SDK0.3.0 supports the independently accepted ArrowFM observational preview: 2–500 rows and2–20 finite continuous variables, original native DAG decoder and separate scores. The service catalogue reports exact task/version/readiness. CDFM also qualifies this input envelope with original native adaptive threshold, separate native scores and directed output, including possible cycles/reciprocals. Select model="cdfm" through the same client interfaces; it does not relabel an acyclic directed result as a declared DAG. AVICI also qualifies this input envelope through model="avici", preserving full observations, native population z-score, strict probability>0.5 decoding and directed cycles/reciprocals. Its native PRNG key stays0; request seeds are retained provenance. It also refuses every non-declared-DAG Ergodic conversion. Native scores are not calibrated causal confidence or effects. Other causal engines and a usable public Ergodic co-release remain gated. No automatic Ergodic/model download is performed by these interfaces.

Client and AsyncClient provide submit_discover(dataset_id, model='arrow', configuration='default', seed=0, constraints=None, model_version=None, idempotency_key=None) and blocking/awaitable discover(..., timeout=600). Submission returns the existing durable Job/AsyncJob handle. A successful discover job returns CausalResult; successful supervised jobs retain their current return types. Timeout does not cancel server work; reconnect with client.job(job_id).

Use target-free observational datasets: discovery preserves every uploaded column in original order. It rejects target-labelled datasets instead of dropping their target. Nonempty constraints and unsupported input reject before model work. Capability/readiness checks come from the current /v1/models task profile, not a client-side list or a model being present in the catalogue.

from welt import Client

# X is the user's finite observational DataFrame. Use this only after the selected
# causal model/task is enabled and its published input bounds are satisfied.
with Client() as client:
    profile = next(m for m in client.models() if m["id"] == "arrow")
    ready = any(p["task"] == "causal_discovery" and p["execution_available"]
                for p in profile["task_profiles"])
    if ready:
        dataset = client.upload(columns=list(X.columns), rows=X.values.tolist(), target=None)
        job = client.submit_discover(dataset["id"], model="arrow", seed=42)
        result = job.result()
        scores = client.causal_scores(job.id)
        reopened = client.causal_result(job.id)

CausalDiscovery has sklearn-style constructor parameters and cloning/get_params; its modelling operation is discover, not supervised fit/predict. discover(X) and submit_discover(X) accept DataFrame/2D arrays/CSV/Parquet via the existing table converter, or keyword dataset_id without upload. Supply exactly one table or reference. Arrays receive x0,x1,... names; named columns retain their original order. Blocking discover closes its temporary HTTP client. A submitted job owns its client until the caller finishes/reconnects and closes job.client explicitly.

CausalResult preserves immutable ordered variables, marks, native graph type, model/configuration and opaque score reference; to_dict returns a fresh ordinary wire dictionary. Immutable job_id/diagnostics/configuration_version/assumptions/ decoder accessors expose original provenance. Blocking convenience calls return result.job_id, so a new client can fetch scores or reopen after the facade closes its HTTP client. Client reopening verifies this owned job identity. Use explicit client.causal_result(job_id)/causal_scores(job_id) for authenticated reconnecting/score access, so the result carries no hidden client or credentials. Score access checks result identity/reference/original axes and finite native probability semantics; scores never threshold/rewrite the graph. Typed malformed-result errors apply without repair.

result.to_ergodic() converts a declared native DAG locally, retains every isolate and native tail/arrow direction. It refuses every graph not explicitly declared a native DAG, including acyclic directed graphs and cycles, without projection. The actual compatible Ergodic0.1.1 co-release wheel has been checked privately. Its repository is private and the public package-index name currently refers to another project; there is no public causal extra yet. Install only the approved co-release artifact when available. Missing/unrelated dependency has typed OptionalDependencyError before importing an unrelated package. Welt never downloads a causal dependency during conversion.

MixedGraph stores node sets: inspect adjacency with graph.to_adjacency(order=list(result.variables)) to retain original axis order. Conversion and local identification/effect estimation are separate scientific steps; a model's discovered edges do not establish causal truth. Native edge scores are neither calibrated confidence nor graph posterior probabilities. Primary graph/score retention/deletion and result quotas are server contracts, independent of seven-day downloadable prediction payloads.

Explicit owned lifecycle

client.delete_causal_result(result.id) explicitly deletes the retained primary graph and scores. client.delete_dataset(result.dataset_id) separately deletes an upload only if it has no retained predictor/result or queued/running job dependencies. AsyncClient exposes the same methods with await. There is no implicit deletion, cascade or expiry when a client closes or discovery times out. These explicit server routes are deployed; dependency/race/tenant contracts were checked against temporary local state. Live acceptance retains its synthetic results and does not delete hosted state.

A successful deletion returns None; repeat deletion of the same owned resource is safe. A foreign or missing identity raises NotFoundError. Dataset dependencies raise ConflictError with code dependency_conflict. Explicitly deleted primary result/score reads raise ResultDeletedError, while job/provenance/usage metadata remain. If payload removal returns retryable deletion_pending, retry the same DELETE; quota and dataset dependency remain reserved until it completes.