statspai.msm

msm

Marginal Structural Models (MSM) for time-varying treatments.

Implements Robins' inverse-probability-of-treatment-weighted (IPTW) estimator for longitudinal data with time-varying confounding.

MarginalStructuralModel

Class wrapper around :func:msm for programmatic access.

stabilized_weights

stabilized_weights(data: DataFrame, treat: str, id: str, time: str, time_varying: List[str], baseline: Optional[List[str]] = None, treat_type: str = 'auto', trim_per_period: float = 0.0) -> ndarray

Compute stabilized IPTW weights for time-varying treatments.

Returns one weight per row (long-format). For treatment :math:A_t with history :math:\bar{A}_{t-1}, time-varying confounders :math:\bar{L}_t (must be pre-treatment), and baseline V:

• Numerator: conditional density/probability of :math:A_t given :math:\bar{A}_{t-1} and V (no L).
• Denominator: conditional density/probability of :math:A_t given :math:\bar{A}_{t-1}, :math:\bar{L}_t, and V.

The row weight is the cumulative product of per-period ratios up to and including period t.

Parameters:

Name	Type	Description	Default
data	DataFrame	Sorted long-format panel (one row per unit-time).	required
treat	str	Treatment column (binary 0/1 or continuous).	required
id	str	Unit and time identifiers.	required
time	str	Unit and time identifiers.	required
time_varying	list of str	Time-varying confounders (already lagged to pre-treatment).	required
baseline	list of str	Baseline covariates.	None
treat_type	(auto, binary, continuous)	Overrides auto-detection.	'auto'
trim_per_period	float	If > 0, trim each period's density ratio at the symmetric quantile [trim_per_period, 1 - trim_per_period] before taking the cumulative product. A common value is 0.01. Set to 0 to disable per-period trimming (the default).	0.0

Returns:

Type	Description
ndarray	Stabilized weight for each row.