statspai.transport

transport

Transportability (sp.transport): generalize causal effects across populations. Combines Pearl-Bareinboim identification (selection diagrams) with Dahabreh-Stuart-style density-ratio weighting.

Quick start

import statspai as sp g = sp.dag("S -> X; X -> Y; S -> Y") sp.transport.identify_transport(g, treatment="X", outcome="Y", ... selection_nodes={"S"}) sp.transport.weights(source=rct, target=target_df, ... features=["age", "sex"], ... treatment="treat", outcome="y")

EvidenceSynthesisResult dataclass

Output of :func:synthesise_evidence.

HeterogeneityResult dataclass

Effect-heterogeneity diagnostic.

ConcordanceResult dataclass

RCT-vs-RWD concordance report.

weights

weights(source: DataFrame, target: DataFrame, features: Sequence[str], treatment: str, outcome: str, truncate: tuple[float, float] | None = (0.01, 0.99)) -> TransportWeightResult

Compute transport weights and a transported ATE.

Parameters:

Name	Type	Description	Default
source	DataFrame	Source (must contain features, treatment, outcome) and target (must contain features).	required
target	DataFrame	Source (must contain features, treatment, outcome) and target (must contain features).	required
features	list[str]	Baseline covariates shared by both populations that identify target-conditional exchangeability.	required
treatment	str	Column names in source.	required
outcome	str	Column names in source.	required
truncate	tuple[float, float] | None	Weight quantiles for truncation.	(0.01, 0.99)

Returns:

Type	Description
TransportWeightResult

identify_transport

identify_transport(dag, treatment, outcome, selection_nodes) -> TransportIdentificationResult

Test s-admissibility of some conditioning set.

Parameters:

Name	Type	Description	Default
dag	DAG	Causal graph.	required
treatment	str | Iterable[str]	Intervention set X.	required
outcome	str | Iterable[str]	Outcome set Y.	required
selection_nodes	Iterable[str]	Nodes (S1, S2, ...) pointing to variables whose distributions differ across source and target populations.	required

Returns:

Type	Description
TransportIdentificationResult

synthesise_evidence

synthesise_evidence(*, rct_estimate: float, rct_se: float, rwd_estimate: float, rwd_se: float, transport_shift: float = 0.0, transport_shift_se: float = 0.0, alpha: float = 0.05, weight_mode: str = 'inverse_variance') -> EvidenceSynthesisResult

Pool an RCT and an RWD estimate into a single evidence-synthesis.

Parameters:

Name	Type	Description	Default
rct_estimate	float	RCT effect estimate and its SE.	required
rct_se	float	RCT effect estimate and its SE.	required
rwd_estimate	float	RWD effect estimate and its SE (already transport-weighted if that is the design).	required
rwd_se	float	RWD effect estimate and its SE (already transport-weighted if that is the design).	required
transport_shift	float	Additive adjustment applied to the RCT estimate to transport it to the RWD target population (e.g. using density-ratio weights).	0.0
transport_shift_se	float	SE of the transport shift, added in quadrature to the RCT SE.	0.0
alpha	float		0.05
weight_mode	('inverse_variance', 'rct_heavy')		'inverse_variance'

Returns:

Type	Description
EvidenceSynthesisResult

References

Dahabreh et al. (2020); Yang, Gamalo & Fu (arXiv:2511.19735, 2025). [@yang2025integrating]

heterogeneity_of_effect

heterogeneity_of_effect(estimates: Sequence[float], ses: Sequence[float]) -> HeterogeneityResult

Effect-heterogeneity diagnostic (DerSimonian-Laird).

Parameters:

Name	Type	Description	Default
estimates	sequence of float		required
ses	sequence of float		required

Returns:

Type	Description
HeterogeneityResult

rwd_rct_concordance

rwd_rct_concordance(*, rct_estimate: float, rct_se: float, rwd_estimate: float, alpha: float = 0.05) -> ConcordanceResult

Report-card metric for whether an RWD estimate agrees with the RCT.

Parameters:

Name	Type	Description	Default
rct_estimate	float		required
rct_se	float		required
rwd_estimate	float		required
alpha	float		0.05

Returns:

Type	Description
ConcordanceResult