causalis.data_contracts.multicausal_estimate.MultiCausalEstimateMultiCausalEstimate
Bases: pydantic.BaseModel
Result container for causal effect estimates.
Parameters
- estimandstr
The estimand being estimated (e.g., ‘ATE’, ‘ATTE’, ‘CATE’).
- modelstr
The name of the model used for estimation.
- model_optionsdict
Options passed to the model.
- valuefloat
The estimated absolute effect.
- ci_upper_absolutefloat
Upper bound of the absolute confidence interval.
- ci_lower_absolutefloat
Lower bound of the absolute confidence interval.
- value_relativefloat, optional
The estimated relative effect.
- ci_upper_relativefloat, optional
Upper bound of the relative confidence interval.
- ci_lower_relativefloat, optional
Lower bound of the relative confidence interval.
- alphafloat
The significance level (e.g., 0.05).
- p_valuefloat, optional
The p-value from the test.
- is_significantbool
Whether the result is statistically significant at alpha.
- n_treatedint
Number of units in the treatment group.
- n_controlint
Number of units in the control group.
- outcomestr
The name of the outcome variable.
- treatmentstr
The name of the treatment variable.
- confounderslist of str, optional
The names of the confounders used in the model.
- timestr
The date when the estimate was created (YYYY-MM-DD).
- diagnostic_dataDiagnosticData, optional
Additional diagnostic data_contracts.
Initialization
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
Canonical target
causalis.data_contracts.multicausal_estimate.MultiCausalEstimate
Sections