Object index

This consolidated autosummary inventory generates the individual object pages used by the categorized API tables.

cleverly.CausalStudy	Validate observed data against an immutable causal-study design.
cleverly.StudyProtocol	Record the scientific protocol that gives a causal question its context.
cleverly.PointTreatment	Declare column roles for a point-treatment study.
cleverly.LongitudinalTreatment	Declare time-ordered roles for a longitudinal study.
cleverly.Repeated	Declare a repeated outcome: one non-absorbing outcome column per time point.
cleverly.TimeToEvent	Declare a baseline treatment with one follow-up time and one event code per unit.
cleverly.LongPanel	Declare a longitudinal study read from a long panel: one row per unit and time.
cleverly.longitudinal.PanelLayout	The long-panel roles a container was built from.
cleverly.longitudinal.LongitudinalData.from_long_panel	Build from a long panel: one row per unit and decision time.
cleverly.longitudinal.LongitudinalData.to_long_panel	Return the long panel this container holds: one row per unit and node.
cleverly.Estimand	Define the public contract for a typed causal question.
cleverly.IdentificationProvider	Convert a causal estimand into an observed-data functional.
cleverly.ExplicitAdjustmentProvider	Identify effects from declared adjustment sets or sequential histories.
cleverly.IdentifiedEffect	Bind an estimand to a functional and its identification assumptions.
cleverly.CausalResult	Define operations shared by fitted causal-result families.
cleverly.estimators.TMLEResult	Store a fitted point-treatment TMLE result.
cleverly.longitudinal.LongitudinalResult	Store estimates and fitted artifacts for longitudinal regimens.
cleverly.fluctuation.Fluctuation	The result of a targeting step.
cleverly.fluctuation.FoldFluctuation	One validation fold's contribution to a cross-validated targeting step.
cleverly.ParameterKey	Describe the structured identity behind a parameter alias.
cleverly.Provenance	Enough to tell whether two results came from the same place.
cleverly.ATE	Compare population counterfactual means against one reference arm.
cleverly.ATT	Compare counterfactual means among units in each comparison arm.
cleverly.ATC	Compare counterfactual means among units in the reference arm.
cleverly.BackdoorMeanContrast	Store a normalized observed-data functional for an estimator adapter.
cleverly.CounterfactualMean	Request counterfactual outcome means by treatment level.
cleverly.NaturalCourseMean	Request the observed-course outcome mean.
cleverly.PopulationAttributableRisk	Request the natural-course risk minus a reference counterfactual risk.
cleverly.PopulationAttributableFraction	Request the preventable fraction relative to the natural-course risk.
cleverly.RiskRatio	Compare counterfactual risks on the ratio scale.
cleverly.OddsRatio	Compare counterfactual odds for a binary outcome.
cleverly.RegimeMean	Request mean outcomes under declared treatment regimens.
cleverly.RegimeContrast	Compare each declared regimen against one reference regimen.
cleverly.LearnedRuleValue	Request the fold-average value of a treatment rule learned inside each training fold.
cleverly.ModifiedTreatmentPolicy	Request mean outcomes under modified treatment policies.
cleverly.ModifiedTreatmentPolicyEffect	Compare modified treatment policies.
cleverly.targets.Interpretation	One causal reading of an identified functional, with the assumptions it adds.
cleverly.IncrementalMean	Request means under incremental propensity-score interventions.
cleverly.IncrementalEffect	Compare incremental propensity-score interventions.
cleverly.MSMProjection	Project counterfactual means onto a marginal structural model.
cleverly.ControlledDirectEffect	Compare treatments while fixing a binary intermediate variable.
cleverly.EstimationMethod	Define the contract for an evidenced estimation method.
cleverly.TMLEMethod	Normalized configuration for the built-in analytic TMLE method.
cleverly.CollaborativeTMLEMethod	Configure collaborative treatment-mechanism selection.
cleverly.DRTMLEMethod	Configure the reduced-dimension DR-TMLE correction.
cleverly.SDRMethod	Configure the sequentially doubly robust longitudinal estimator.
cleverly.IPSIMethod	Configure Kennedy's estimator of incremental propensity-score interventions.
cleverly.GComputationMethod	Configure sequential g-computation of a longitudinal regimen.
cleverly.IPWMethod	Configure sequential inverse probability weighting of a longitudinal regimen.
cleverly.ParametricGFormulaMethod	Configure the parametric g-formula of a longitudinal regimen.
cleverly.ModelSpec	Configure nuisance learning for TMLE methods.
cleverly.CrossFitting	Configure outer and learner-level sample splitting.
cleverly.SplitPlan	Reusable cross-fitting assignments for every repeat.
cleverly.Targeting	Configure fluctuation, nuisance bounds, and solver behavior.
cleverly.Inference	Configure confidence intervals and resampling.
cleverly.Runtime	Configure reproducibility, provenance, and resources.
cleverly.MethodAvailability	Report whether a method can estimate an identified effect.
cleverly.ParameterEstimate	A point estimate with everything needed to do inference on it.
cleverly.AssessmentCapability	Describe whether and how a result supports one assessment operation.
cleverly.AssessmentReport	Collect validation, diagnostics, and sensitivity in one post-fit battery.
cleverly.AssessmentStatus	Status returned by a diagnostic or validation operation.
cleverly.DiagnosticReport	Collect statuses from a combined diagnostic or sensitivity run.
cleverly.ValidationReport	Collect results from the inexpensive validation battery.
cleverly.Replayability	Describe which post-fit actions a saved result can reproduce.
cleverly.VariableImportanceEntry	One candidate exposure's target estimate and declared adjustment set.
cleverly.VariableImportanceResult	Candidate-exposure estimates with multiplicity-adjusted null tests.
cleverly.variable_importance	Estimate one causal importance parameter per candidate exposure.
cleverly.load	Load a complete result from a trusted joblib artifact.
cleverly.SuperLearner	Cross-validated convex ensemble of a library of learners.
cleverly.CleverlyError	Base class for every error raised by cleverly.
cleverly.CapabilityError	A request the package refuses for a reason other than the data alone.
cleverly.DataError	The supplied data violates an assumption the estimator relies on.
cleverly.MethodConfigurationError	An estimation-method declaration is invalid or cannot take effect.
cleverly.NotFittedError	A result was requested from an estimator that has not been fitted.
cleverly.ConvergenceWarning	The targeting step stopped before reaching the requested tolerance.
cleverly.MonteCarloWarning	The simulation error is a large share of a parametric g-formula interval.
cleverly.ParameterSpaceWarning	An estimate left the range of the parameter it estimates.
cleverly.PositivityWarning	Estimated treatment (or missingness) probabilities are near 0 or 1.
cleverly.SparseInfluenceWarning	A few rows carry the influence curve, so the Wald interval may under-cover.
cleverly.VersionMismatchWarning	A saved result was written by a different cleverly version than the one loading it.
cleverly.WeightingWarning	The supplied observation weights may not mean what the estimator assumes.
cleverly.exceptions.Refusal	One refusal: its reason code, the failed condition, and what to do instead.
cleverly.exceptions.RefusalReason	Why the package refuses a request.
cleverly.learners.SuperLearnerDiagnostics	What the ensemble learned, for reporting and validation.
cleverly.learners.CorrelationScreener	scikit-learn selector wrapping screen_by_correlation().
cleverly.learners.ParametricOutcome	An unpenalized logistic GLM of the outcome on a declared feature map.
cleverly.learners.ParametricMechanism	An unpenalized logistic regression of an indicator on a polynomial in logit q.
cleverly.learners.ParametricFitDiagnostics	What one parametric fit reports.
cleverly.learners.CrossFitPlan	The fold policy a fit declared, as distinct from the split it got.
cleverly.learners.Folds	A cross-fitting partition.
cleverly.learners.FoldOrigin	The record of how random_partition() drew one split.
cleverly.learners.ConditionalDensity	An evaluated conditional density: one row of bin probabilities per observation.
cleverly.learners.DensityDiagnostics	What the density fit looked like, for the report and for the positivity check.
cleverly.learners.default_library	Return the concrete default ensemble for one prediction task.
cleverly.learners.make_folds	Build a cross-fitting partition of n observations.
cleverly.learners.random_partition	Draw an unstratified split of rows, or of whole clusters, from a seed.
cleverly.learners.thread_limit	Limit native thread pools for the duration of the block.
cleverly.learners.set_thread_limit	Set the native thread limit applied around each nuisance-model fit.
cleverly.learners.get_thread_limit	The thread limit currently applied around each fit.
cleverly.interventions.Intervention	A conditional distribution over the treatment arms.
cleverly.interventions.Static	Set the treatment to one level for everybody: \(g^\star = \mathbb 1\{a = v\}\).
cleverly.interventions.Rule	A dynamic regime: assign \(d(W)\), a deterministic function of the covariates.
cleverly.interventions.Stochastic	A known stochastic regime: assign arm \(a\) with probability \(g^\star(a \mid W)\).
cleverly.longitudinal.DynamicRegimen	A plan whose nodes may be rules rather than constants.
cleverly.longitudinal.each_node	Write one rule for every node, each reading its own node's columns by name.
cleverly.longitudinal.NodeRule	One node of a rule written for every node: calls rule(frame, time).
cleverly.interventions.LearnedRule	Declare the rule whose fold-average value a fit estimates.
cleverly.interventions.LearnedRuleRecord	Record what a learned-rule fit estimated, under result.extra["learned_rule"].
cleverly.interventions.RegimeSet	The regimes a fit targets, and their evaluated densities.
cleverly.interventions.Incremental	Multiply the odds of treatment levels by declared factors.
cleverly.interventions.IPSISet	Every declared tilt, evaluated against the estimated mechanism.
cleverly.interventions.Shift	Add delta to everyone's treatment, up to cap.
cleverly.interventions.Scale	Multiply everyone's dose by factor, up to cap.
cleverly.interventions.DoseQuantile	The cap at a quantile of the natural dose among the units at risk.
cleverly.interventions.SupportBoundary	The cap at a learned conditional quantile of the dose: c(h).
cleverly.interventions.LinearQuantileRegression	A declared linear quantile regression: the boundary beta' (1, features).
cleverly.interventions.Piecewise	A policy that applies a different shift or scale on each interval of the dose.
cleverly.interventions.Piece	One interval of a declared policy and the strictly monotone map it applies there.
cleverly.interventions.Support	The declared conditional support of a continuous dose: [lower(h), upper(h)].
cleverly.interventions.ModifiedPolicy	A modified treatment policy that the analyst declares.
cleverly.interventions.Randomizer	A finite, known law for the randomizer of a randomized policy.
cleverly.interventions.RiskRatioTilt	The risk-ratio incremental intervention of lmtp::ipsi, a randomized policy.
cleverly.interventions.JointPolicy	A modified treatment policy on a vector node, declared one component at a time.
cleverly.interventions.PolicySet	Every declared policy, evaluated on the data and on the estimated mechanism.
cleverly.interventions.SupportReport	RegimeSupport for every regime in a fit, plus the worst case.
cleverly.interventions.RegimeSupport	Overlap for one regime.
cleverly.interventions.IncrementalSupport	Overlap for one tilt -- which for this estimand is a statement, not a warning.
cleverly.interventions.PolicySupport	Overlap for one policy: how hard the density ratio is working, and where it fails.
cleverly.interventions.check_support	Overlap diagnostics for each regime, from the untruncated mechanism.
cleverly.interventions.check_incremental_support	Per-tilt overlap, in the vocabulary the other two axes' reports use.
cleverly.interventions.check_policy_support	Per-policy overlap, in the vocabulary cleverly.interventions.support uses.
cleverly.assessment.SensitivityFacade	Access sensitivity analyses supported by a fitted causal result.
cleverly.sensitivity.PositivityReport	Overlap diagnostics for a fitted TMLE.
cleverly.sensitivity.SensitivityBounds	Bias-adjusted bounds under an assumed confounder strength.
cleverly.sensitivity.SensitivityElements	The ingredients of the bias bound for one estimand.
cleverly.sensitivity.BenchmarkResult	Confounder strengths calibrated against covariates that were observed.
cleverly.sensitivity.ConfounderStrengthGrid	Declare treatment and outcome perturbation strengths.
cleverly.sensitivity.ObservedConfounderCalibration	Record one model-dependent observed-covariate calibration.
cleverly.sensitivity.SimulatedConfoundingCell	Store one strength pair's estimate or structured failure.
cleverly.sensitivity.SimulatedConfoundingResult	Store a qualitative simulated-confounding stress surface.
cleverly.sensitivity.EValue	E-values for a point estimate and the confidence limit nearest the null.
cleverly.sensitivity.positivity_report	Compute overlap diagnostics for a fitted TMLE.
cleverly.sensitivity.truncation_curve	Re-estimate across a grid of truncation bounds.
cleverly.sensitivity.omitted_variable_bounds	Bias-adjusted bounds and robustness values for one estimand.
cleverly.sensitivity.robustness_value	The confounding strength that would explain the effect away.
cleverly.sensitivity.benchmark	Calibrate cf_y and cf_d against observed covariates.
cleverly.sensitivity.evalue.evalue	E-value for a fitted result.
cleverly.sensitivity.missingness_tilt	Estimate under a range of departures from missingness-at-random.
cleverly.sensitivity.tipping_gamma	The tilt at which the conclusion tips.
cleverly.sensitivity.simulated_confounding	Refit a selected parameter across a simulated common-cause strength grid.
cleverly.assessment.DiagnosticsFacade	Access diagnostics supported by a fitted causal result.
cleverly.validation.ScoreCheck	Whether the targeting step solved the score equations it was supposed to.
cleverly.validation.ScoreCheckRow	The score diagnostic for one estimand or one targeted family.
cleverly.validation.NuisanceDiagnostics	Fit quality for every nuisance model in a TMLE fit.
cleverly.validation.NuisanceModelReport	Fit quality for one nuisance model.
cleverly.validation.RepeatSpreadRow	Split sensitivity for one parameter in a repeated cross-fitted result.
cleverly.validation.GaussianNoise	Declare one Gaussian noise law for a generated outcome.
cleverly.validation.GaussianIndependentOutcome	Declare an outcome independent of treatment and adjustment variables.
cleverly.validation.GaussianAdjustmentOutcome	Declare f(W) + effect * A + epsilon with Gaussian noise.
cleverly.validation.RelativeGaussianNoise	Declare mean-zero numeric noise relative to a sampled variable.
cleverly.validation.BootstrapMeasurementError	Declare bootstrap measurement-error validation for adjustment variables.
cleverly.validation.EmpiricalInclusionRule	Declare a two-sided empirical inclusion rule for generated outcomes.
cleverly.validation.EmpiricalRefitRecord	One successful empirical refit.
cleverly.validation.RefutationTest	The outcome of one refutation test.
cleverly.validation.RefutationResult	All refutation tests run on a fit.
cleverly.validation.CoverageStudy	Repeatedly fit an estimator on fresh data and measure bias and coverage.
cleverly.validation.StudyResult	The full output of a CoverageStudy.
cleverly.validation.ReplicationRecord	One estimand from one successful repeated-sampling draw.
cleverly.validation.ReplicationFailure	A replication whose fit failed numerically, kept so the study can be diagnosed.
cleverly.validation.ReplicationCheckpoint	Storage that lets an interrupted CoverageStudy resume.
cleverly.validation.DirectoryCheckpoint	Keep each finished replication of a CoverageStudy as a file in a directory.
cleverly.validation.score_check	Check that targeting solved the score equation.
cleverly.validation.nuisance_diagnostics	Fit diagnostics for every nuisance model in the fit.
cleverly.validation.refute	Run refutation tests against a fitted model.
cleverly.inference.BootstrapResult	Bootstrap draws for every estimand, plus how many replicates failed.
cleverly.inference.SimultaneousBands	Joint confidence bands across several estimands.
cleverly.inference.delta_method	Influence curve of function applied to several estimands.
cleverly.inference.influence_covariance	Covariance matrix across several estimands' influence curves.
cleverly.inference.influence_variance	Variance of an estimator from its influence curve.
cleverly.inference.multiplier_critical_value	The 1 - alpha quantile of the max-t statistic.
cleverly.inference.simultaneous_bands	Joint confidence bands over a set of estimates.
cleverly.inference.run_bootstrap	Run n_replicates full-refit bootstrap replicates.
cleverly.datasets.available	Names of the bundled data-generating processes.
cleverly.datasets.make_linear_ate	Generate point-treatment data with a constant additive effect.
cleverly.datasets.make_nonlinear_ate	Nonlinear nuisance functions and a heterogeneous effect.
cleverly.datasets.make_nonlinear_bounded	Generate a bounded outcome with nonlinear nuisance functions.
cleverly.datasets.make_binary_outcome	A binary outcome with known risks, risk ratio and odds ratio.
cleverly.datasets.make_multi_arm	A three-armed confounded process with known counterfactual means.
cleverly.datasets.make_heterogeneous	A sample whose treated and control populations have genuinely different effects.
cleverly.datasets.make_instrument	Instrument-inflation: a covariate that predicts treatment but not the outcome.
cleverly.datasets.make_shift_dose	A confounded continuous dose with known means under each declared shift.
cleverly.datasets.make_missing_outcome	Missing outcomes, with a missingness mechanism that depends on treatment.
cleverly.datasets.make_missing_outcome_binary	A binary outcome with outcomes missing at random given (A, W).
cleverly.datasets.make_missing_outcome_bounded	Missing outcomes on the known support (0, 1), with a nonlinear outcome mean.
cleverly.datasets.make_cde	Controlled direct effect: the effect of A with Z held fixed.
cleverly.datasets.make_clustered	Clustered data with an unobserved shared effect modifying the treatment effect.
cleverly.datasets.make_biased_sample	A sample selected with known, unequal probabilities from a known population.
cleverly.datasets.make_weak_overlap	A practical positivity violation.
cleverly.datasets.make_longitudinal	Two time points, a binary outcome and (optionally) monotone censoring.
cleverly.datasets.make_longitudinal_survival	Two time points, an absorbing event at each, and monotone censoring.
cleverly.datasets.make_longitudinal_repeated	Three time points, a repeated binary outcome at each, and monotone censoring.
cleverly.datasets.make_longitudinal_competing	Two time points, two competing absorbing causes at each, monotone censoring.
cleverly.datasets.make_longitudinal_weighted	A biased sample of make_longitudinal(), with the design weights that undo it.
cleverly.datasets.make_point_survival	A baseline treatment, a discrete-time event, and dropout at the visits.
cleverly.datasets.point_survival_truth	The exact truth of make_point_survival() under an intervention on \(A\).
cleverly.datasets.navigation_data	Draw the point-treatment program data, with the columns under the program's names.
cleverly.datasets.navigation_protocol	Return the protocol for the standard navigation offer in the tutorial program.
cleverly.datasets.longitudinal_navigation_protocol	Return the protocol for navigation offered at two decisions after discharge.
cleverly.datasets.make_sequential_offers	Draw a weekly-offer campaign as a long panel, with the exact truth of its plans.
cleverly.datasets.offers_truth	Return the exact week-4 full-price share under each plan, and each contrast with no offers.
cleverly.datasets.offers_protocol	Return the protocol of the weekly-offer campaign.
cleverly.datasets.win_back	Return the win-back offer: the discount for a customer who is not engaged, else nothing.
cleverly.datasets.win_back_then_ship	Return the win-back-then-ship offer: the discount if not engaged, else free shipping.
cleverly.datasets.make_sequential_pricing	Draw three monthly renewal prices and the renewal, with the exact truth of the plans.
cleverly.datasets.pricing_truth	Return the exact mean renewal and revenue under each plan, and the effect of each plan.
cleverly.datasets.pricing_protocol	Return the protocol of the monthly renewal-price program.
cleverly.datasets.price_rise	Return the plan that raises each price by factor and keeps the price in the band.