statspai.dose_response

dose_response

Continuous Treatment Effects: Dose-Response Estimation.

Estimates the dose-response function E[Y(t)] for continuous treatments using the generalized propensity score (GPS) approach and kernel-based methods.

References

Hirano, K. & Imbens, G. W. (2004). The Propensity Score with Continuous Treatments. Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives, 226164, 73-84. [@hirano2004propensity]

Kennedy, E. H., Ma, Z., McHugh, M. D., & Small, D. S. (2017). Non-parametric methods for doubly robust estimation of continuous treatment effects. JRSS-B, 79(4), 1229-1245. [@kennedy2017parametric]

DoseResponse

Generalized Propensity Score dose-response estimator.

Parameters:

Name	Type	Description	Default
data	DataFrame		required
y	str		required
treat	str		required
covariates	list of str		required
n_dose_points	int		20
dose_range	tuple		None
treatment_model	sklearn estimator		None
outcome_model	sklearn estimator		None
n_bootstrap	int		200
alpha	float		0.05
random_state	int		42

fit

fit() -> CausalResult

Estimate the dose-response function.

dose_response

dose_response(data: DataFrame, y: str, treat: str, covariates: List[str], n_dose_points: int = 20, dose_range: Optional[Tuple[float, float]] = None, treatment_model: Optional[BaseEstimator] = None, outcome_model: Optional[BaseEstimator] = None, n_bootstrap: int = 200, alpha: float = 0.05, random_state: int = 42) -> CausalResult

Estimate the dose-response function for a continuous treatment.

Parameters:

Name	Type	Description	Default
data	DataFrame	Input data.	required
y	str	Outcome variable.	required
treat	str	Continuous treatment variable.	required
covariates	list of str	Covariate names.	required
n_dose_points	int	Number of dose levels to evaluate.	20
dose_range	tuple of (float, float)	(min, max) dose range. If None, uses 5th-95th percentile.	None
treatment_model	sklearn estimator	Model for E[T|X]. Default: GBM.	None
outcome_model	sklearn estimator	Model for E[Y|T, GPS]. Default: GBM.	None
n_bootstrap	int	Bootstrap iterations for confidence bands.	200
alpha	float	Significance level.	0.05
random_state	int		42

Returns:

Type	Description
CausalResult	detail contains the dose-response curve: columns 'dose', 'response', 'se', 'ci_lower', 'ci_upper'. model_info contains 'avg_marginal_effect' (derivative).

Examples:

>>> import statspai as sp
>>> result = sp.dose_response(df, y='outcome', treat='dosage',
...                           covariates=['age', 'weight'])
>>> print(result.detail)  # dose-response curve

scigan

scigan(data: DataFrame, y: str, treatment: str, covariates: Sequence[str], t_grid: Optional[Sequence[float]] = None, propensity_weights: Optional[ndarray] = None, **kwargs) -> VCNetResult

Adversarial dose-response estimator (Bica et al. 2020).

The reference SCIGAN architecture is a GAN — too heavy to ship as a dependency-free algorithm. This entry point exposes a propensity-weighted VCNet fit that captures the essential "balancing" behaviour of SCIGAN: residual imbalance along the treatment axis is re-weighted using a user-provided propensity_weights (g(T | X)^{-1}). For the full SCIGAN training loop, plug in your own GAN-generated counterfactual samples and pass the re-weighting through propensity_weights.