statspai.multi_treatment

multi_treatment

Multi-valued Treatment Effects.

Estimates causal effects when the treatment takes more than two values (e.g., different drug dosages, multiple policy options).

Uses inverse probability weighting with multinomial propensity scores.

References

Cattaneo, M. D. (2010). Efficient semiparametric estimation of multi-valued treatment effects. Journal of Econometrics, 155(2), 138-154. [@cattaneo2010efficient]

Lechner, M. (2001). Identification and estimation of causal effects of multiple treatments under the conditional independence assumption. Econometric Evaluation of Labour Market Policies, 43-58. [@lechner2001identification]

MultiTreatment

Multi-valued treatment effects estimator.

fit

fit() -> CausalResult

Estimate multi-valued treatment effects.

multi_treatment

multi_treatment(data: DataFrame, y: str, treat: str, covariates: List[str], reference: Optional[int] = None, n_bootstrap: int = 500, alpha: float = 0.05, random_state: int = 42) -> CausalResult

Estimate effects of multi-valued treatments via AIPW.

Parameters:

Name	Type	Description	Default
data	DataFrame	Input data.	required
y	str	Outcome variable.	required
treat	str	Treatment variable with K+1 levels (0, 1, ..., K).	required
covariates	list of str	Covariate names.	required
reference	int	Reference treatment level (control). Default: minimum value.	None
n_bootstrap	int		500
alpha	float		0.05
random_state	int		42

Returns:

Type	Description
CausalResult	detail DataFrame has pairwise effects vs reference. model_info contains all pairwise contrasts.

Examples:

>>> import statspai as sp
>>> # Treatment with 3 levels: 0 (control), 1 (low), 2 (high)
>>> result = sp.multi_treatment(df, y='outcome', treat='dose_level',
...                             covariates=['age', 'weight'])
>>> print(result.detail)  # effects vs control