`statspai.longitudinal`¶

longitudinal ¶

Longitudinal causal inference (sp.longitudinal).

Unified entry for What If Layer-4 methods (time-varying treatments with time-varying confounders). Wraps MSM / g-formula ICE / IPW under a single dispatcher with a dynamic-regime DSL.

import statspai as sp r = sp.longitudinal.analyze( ... data=panel, ... id="pid", ... time="visit", ... treatment="drug", ... outcome="cd4", ... time_varying=["cd4_lag", "viral_load_lag"], ... baseline=["age", "sex"], ... regime="if cd4_lag < 200 then 1 else 0", ... ) r.summary()

diff = sp.longitudinal.contrast( ... data=panel, id="pid", time="visit", ... treatment="drug", outcome="cd4", ... regime_a="always_treat", ... regime_b="never_treat", ... time_varying=["cd4_lag"], ... )

Regime `dataclass` ¶

A dynamic treatment regime.

Attributes:

Name	Type	Description
`kind`	`{'static', 'dynamic'}`
`name`	`str`
`rule`	`Union[list, Callable]`	Static regimes store a list/sequence; dynamic regimes store a callable `(history_dict, t) -> treatment_value`.

treatment ¶

treatment(history: dict, t: int = 0, K: int = 1)

Evaluate the regime at time t given history.

apply ¶

apply(history_df: DataFrame) -> Series

Vectorized: apply the regime to each row of a history dataframe.

LongitudinalResult `dataclass` ¶

Result of a unified longitudinal analysis.

Attributes:

Name	Type	Description
`method`	`str`	Which estimator was used ("msm", "g-formula", "ipw", "ltmle").
`regime_name`	`str`	Name of the regime being evaluated.
`estimate`	`float`	E[Y(regime)] under the chosen estimator.
`se`	`float`
`ci`	`tuple[float, float]`
`n`	`int`	Panel sample size.
`n_periods`	`int`
`diagnostics`	`dict`	Weight quantiles, positivity flags, etc.
`underlying_result`	`Any`	Raw result object from the underlying estimator.

always_treat ¶

always_treat(K: int = 1) -> Regime

Convenience: the always-treat regime over K periods.

never_treat ¶

never_treat(K: int = 1) -> Regime

Convenience: the never-treat regime over K periods.

contrast ¶

contrast(data: DataFrame, id: str, time: str, treatment: str, outcome: str, regime_a: Union[str, Sequence, Regime], regime_b: Union[str, Sequence, Regime], **kwargs) -> dict

Estimate E[Y(regime_a)] - E[Y(regime_b)] using :func:analyze.

Returns:

Type	Description
`dict`	With keys `regime_a`, `regime_b`, `contrast`, `a_result`, `b_result`. The `contrast` value is the plug-in difference `a.estimate - b.estimate`; its SE uses the delta-method approximation `sqrt(se_a^2 + se_b^2)`.

statspai.longitudinal¶