causalis.shared.outcome_outliers.outcome_outliersoutcome_outliers
Detect outcome outliers per treatment group using IQR or z-score rules.
Parameters
- dataCausalData or MultiCausalData
Causal dataset containing the dataframe and metadata.
- treatmentstr, optional
Treatment column name. For MultiCausalData, if not provided, one-hot treatment columns are converted to assigned treatment labels.
- outcomestr, optional
Outcome column name. Defaults to
data.outcome.- method{“iqr”, “zscore”}, default “iqr”
Outlier detection rule.
- iqr_kfloat, default 1.5
Multiplier for the IQR rule.
- z_threshfloat, default 3.0
Z-score threshold for the z-score rule.
- tail{“both”, “lower”, “upper”}, default “both”
Which tail(s) to flag as outliers.
- return_rowsbool, default False
If True, also return the rows flagged as outliers (subset of
data.df).
Returns
Per-treatment summary with counts, rates, bounds, and flags. outliers : pandas.DataFrame Only returned when return_rows=True. Subset of data.df containing flagged outlier rows.
Notes
Bounds are computed within each treatment group.
Canonical target
causalis.shared.outcome_outliers.outcome_outliers
Sections