Sanity-check a causal claim ("cause drove effect") before acting on it.

Provide exactly one of csv (CSV text), data (list of row objects) or dataset_id (a dataset uploaded via guardian_request_upload; use this for more than ~200 rows; single-use, deleted when reviewed) containing columns named by cause and effect. Returns a verdict -- pass (act), caution (act carefully), or hold (do not act; the effect looks confounded or unsupported) -- with the naive correlational effect versus the confounder-adjusted causal effect and its confidence interval, the confounders adjusted for, a confidence score, a plain-English interpretation, and a recommended action, plus the estimator used and the inferred causal DAG. All numbers are deterministic statistics (never LLM-generated); the LLM may only choose an estimator id from a fixed whitelist and write the interpretation prose.

Args: cause: Treatment column the claim says drives the outcome (e.g. "ad_spend"). effect: Outcome column (e.g. "new_customers"). csv: Dataset as CSV text (header row required). data: Dataset as a list of row records (alternative to csv). dataset_id: ID returned by guardian_request_upload after uploading the CSV (alternative to csv/data; for datasets above ~200 rows). Single-use: the dataset is deleted when reviewed, so re-upload to retry. known_confounders: Columns to adjust for; otherwise discovered automatically. context: Free-text business context, e.g. "Q3 promo evaluation". units: Display-only unit hints by column, e.g. {"ad_spend": "USD"}. step: A concrete treatment step (e.g. 1000 for a $1,000 spend increase) to restate the effect in business terms (cost per incremental outcome, ...). time_col: Column to order rows by for time series (autocorrelation-robust standard errors and adstock). adstock: Opt-in geometric-adstock transform for a lagged/carryover treatment. se: Standard-error flavor; "auto" diagnoses autocorrelation when time_col is given. test_design: true (or an options object) also returns how many periods a spend-cut test needs to confirm the effect. estimator: Primary-effect estimator. Omit (or "auto") to follow config.estimator (default "rule": the deterministic choice); "llm" lets the server's LLM pick from a fixed whitelist (falls back to the rule if no LLM is configured); or force "ols_adjusted", "aipw", "ipw", "psm" or "dml_forest" (always wins over config). The response reports the estimator used (estimator.id, .source, .rationale, .fallback_reason) and the inferred dag. config: Per-step engine config, an object with optional keys cleaning, discovery, estimator, summary, each "rule" (deterministic, no LLM) or "llm". Defaults when omitted or partial: cleaning="llm", discovery="llm", estimator="rule", summary="rule". The LLM never computes a number: cleaning proposes derived columns / null-imputation strategies (pandas computes the values), discovery prunes/orients the discovered DAG (cause/effect are always yours), estimator picks a whitelisted id, summary writes the prose ("rule", the default, is a fixed template that explains the why with no AI). An "llm" step falls back to "rule" when the server has no Gemini key or the call fails; the response's engine_steps reports requested vs used per step with a fallback reason. Unknown keys or values are rejected. Use all "rule" for a fully deterministic, reproducible run. seed: Optional base random seed (default 42) for every seeded step; the seeds used are returned in stamp.seeds. cate: Opt-in list of dataset columns to stratify by for subgroup effects (returned in cate); columns are validated against the dataset. sensitivity: Opt-in; true also returns sensitivity checks (sensitivity).

Every response carries `stamp` (core/guardian versions, estimator, resolved
config, seeds, data_fingerprint) and `llm_decisions` (audit/replay records):
a changed answer with an unchanged `stamp.data_fingerprint` came from a Syl
release or config change, not from your data. Only cleaning/discovery/estimator
set to "llm" can change a number; summary never does.