Syl Guardian

Sanity-check a causal claim ("cause drove effect") before acting on it. Provide exactly one of ``csv`` (CSV text) or ``data`` (list of row objects) 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. All numbers are deterministic statistics (never LLM-generated); only the interpretation prose may be AI-written. 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``). 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.