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Submodule
causalis.scenarios.unconfoundedness.refutation.overlap.overlap_validation

overlap_validation

Submodule causalis.scenarios.unconfoundedness.refutation.overlap.overlap_validation with no child pages and 2 documented members.

Symbol indexAPI membersrun_overlap_diagnostics__all__

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run_overlap_diagnosticsfunction

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function
causalis.scenarios.unconfoundedness.refutation.overlap.overlap_validation.run_overlap_diagnostics

run_overlap_diagnostics

Run overlap and calibration diagnostics for an estimated propensity model.

The core overlap object is the propensity score

m(X)=P(D=1∣X).m(X) = \mathbb{P}(D=1 \mid X).

This diagnostic checks whether estimated propensities stay away from the edges and whether the implied weights are stable. For example, ATE weights use

wi(1)=Dim(Xi),wi(0)=1−Di1−m(Xi),w_i^{(1)} = \frac{D_i}{m(X_i)}, \qquad w_i^{(0)} = \frac{1-D_i}{1-m(X_i)},

so very small m(Xi)m(X_i) or very large m(Xi)m(X_i) can create large leverage points. The report combines:

  • edge mass near 0 and 1,

  • treated/control separation in propensity space (KS, AUC),

  • effective sample size and tail diagnostics for weights,

  • calibration summaries such as ECE, recalibration slope, and intercept.

Parameters

dataCausalData

Dataset used to fit the estimator.

estimateCausalEstimate

Effect estimate with diagnostic_data containing propensity-related arrays such as m_hat and d.

thresholdsdict, optional

Optional threshold overrides keyed by metric name.

n_binsint, default 10

Number of bins used for calibration summaries.

use_hajekbool, optional

Whether to evaluate normalized IPW identities. If omitted, the value is inferred from diagnostic metadata.

return_summarybool, default True

Include a compact tabular summary in the returned payload.

auc_flip_marginfloat, default 0.05

Margin around 0.5 used when flagging reversed treated/control ranking.

Returns

Dict[str, Any]

Diagnostic report containing edge-mass, calibration, weight-stability, and optional summary tables.

Raises

ValueError

If required diagnostic arrays are missing or have incompatible shapes.

Examples

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causalis.scenarios.unconfoundedness.refutation.overlap.overlap_validation.run_overlap_diagnostics

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causalis.scenarios.unconfoundedness.refutation.overlap.overlap_validation.__all__

__all__

Value: ['run_overlap_diagnostics']

[‘run_overlap_diagnostics’]

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causalis.scenarios.unconfoundedness.refutation.overlap.overlap_validation.__all__

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