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causalis
Package entry
causalisRoot package overview and namespace mapNamespaces
causalis.scenarios.cuped.diagnostics.regression_checksSubmodule causalis.scenarios.cuped.diagnostics.regression_checks with no child pages and 55 documented members.
Classes
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Functions
Jump directly into the documented functions for this page.
Data
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causalis.scenarios.cuped.diagnostics.regression_checks.FLAG_GREEN‘GREEN’
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.FLAG_GREEN
causalis.scenarios.cuped.diagnostics.regression_checks.FLAG_YELLOW‘YELLOW’
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causalis.scenarios.cuped.diagnostics.regression_checks.FLAG_YELLOW
causalis.scenarios.cuped.diagnostics.regression_checks.FLAG_RED‘RED’
causalis.scenarios.cuped.diagnostics.regression_checks.FLAG_LEVELNone
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causalis.scenarios.cuped.diagnostics.regression_checks.FLAG_LEVEL
causalis.scenarios.cuped.diagnostics.regression_checks.FLAG_COLORNone
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causalis.scenarios.cuped.diagnostics.regression_checks.FLAG_COLOR
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecksBases: pydantic.BaseModel
Lightweight OLS/regression health checks for CUPED diagnostics.
Initialization
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks
Sections
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.ate_naiveNone
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.ate_naive
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.ate_adjNone
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causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.ate_adj
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.ate_gapNone
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causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.ate_gap
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.ate_gap_over_se_naiveNone
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.ate_gap_over_se_naive
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.kNone
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causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.k
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.rankNone
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causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.rank
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.full_rankNone
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causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.full_rank
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.condition_numberNone
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causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.condition_number
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.p_main_covariatesNone
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.p_main_covariates
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.near_duplicate_pairs‘Field(…)’
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causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.near_duplicate_pairs
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.vifNone
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.vif
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.resid_scale_madNone
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causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.resid_scale_mad
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.n_std_resid_gt_3None
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causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.n_std_resid_gt_3
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.n_std_resid_gt_4None
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causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.n_std_resid_gt_4
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.max_abs_std_residNone
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causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.max_abs_std_resid
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.max_leverageNone
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causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.max_leverage
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.leverage_cutoffNone
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.leverage_cutoff
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.n_high_leverageNone
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.n_high_leverage
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.max_cooksNone
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.max_cooks
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.cooks_cutoffNone
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.cooks_cutoff
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.n_high_cooksNone
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.n_high_cooks
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.min_one_minus_hNone
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.min_one_minus_h
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.n_tiny_one_minus_hNone
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.n_tiny_one_minus_h
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.winsor_qNone
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.winsor_q
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.ate_adj_winsorNone
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causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.ate_adj_winsor
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.ate_adj_winsor_gapNone
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.RegressionChecks.ate_adj_winsor_gap
causalis.scenarios.cuped.diagnostics.regression_checks.design_matrix_checksReturn rank/conditioning diagnostics for a numeric design matrix.
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.design_matrix_checks
causalis.scenarios.cuped.diagnostics.regression_checks.near_duplicate_corr_pairsFind pairs with absolute correlation very close to one.
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.near_duplicate_corr_pairs
causalis.scenarios.cuped.diagnostics.regression_checks.vif_from_corrApproximate VIF from inverse correlation matrix of standardized covariates..
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.vif_from_corr
causalis.scenarios.cuped.diagnostics.regression_checks.leverage_and_cooksCompute leverage, Cook’s distance, and internally studentized residuals.
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.leverage_and_cooks
causalis.scenarios.cuped.diagnostics.regression_checks.winsor_fit_tauRefit OLS on winsorized outcome and return treatment coefficient.
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.winsor_fit_tau
causalis.scenarios.cuped.diagnostics.regression_checks.run_regression_checksBuild a compact payload with design, residual, and influence diagnostics.
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.run_regression_checks
causalis.scenarios.cuped.diagnostics.regression_checks.assumption_design_rankCheck that the design matrix is full rank.
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.assumption_design_rank
causalis.scenarios.cuped.diagnostics.regression_checks.assumption_condition_numberCheck global collinearity via condition number.
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.assumption_condition_number
causalis.scenarios.cuped.diagnostics.regression_checks.assumption_near_duplicatesCheck near-duplicate centered covariate pairs.
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.assumption_near_duplicates
causalis.scenarios.cuped.diagnostics.regression_checks.assumption_vifCheck VIF from centered main-effect covariates.
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.assumption_vif
causalis.scenarios.cuped.diagnostics.regression_checks.assumption_ate_gapCheck adjusted-vs-naive ATE gap relative to naive SE.
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.assumption_ate_gap
causalis.scenarios.cuped.diagnostics.regression_checks.assumption_residual_tailsCheck residual extremes using max standardized residual only.
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.assumption_residual_tails
causalis.scenarios.cuped.diagnostics.regression_checks.assumption_leverageCheck leverage concentration.
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.assumption_leverage
causalis.scenarios.cuped.diagnostics.regression_checks.assumption_cooksCheck Cook’s distance influence diagnostics.
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.assumption_cooks
causalis.scenarios.cuped.diagnostics.regression_checks.assumption_hc23_stabilityCheck HC2/HC3 stability when leverage terms approach one.
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.assumption_hc23_stability
causalis.scenarios.cuped.diagnostics.regression_checks.assumption_winsor_sensitivityCheck sensitivity of adjusted ATE to winsorized-outcome refit.
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.assumption_winsor_sensitivity
causalis.scenarios.cuped.diagnostics.regression_checks.regression_assumption_rows_from_checksRun all CUPED regression assumption tests and return row payloads.
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.regression_assumption_rows_from_checks
causalis.scenarios.cuped.diagnostics.regression_checks.regression_assumptions_table_from_checksReturn a table of GREEN/YELLOW/RED assumption flags from checks payload.
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.regression_assumptions_table_from_checks
causalis.scenarios.cuped.diagnostics.regression_checks.regression_assumptions_table_from_diagnostic_dataBuild assumption table from CUPEDDiagnosticData payload.
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.regression_assumptions_table_from_diagnostic_data
causalis.scenarios.cuped.diagnostics.regression_checks.regression_assumptions_table_from_estimateBuild assumptions table from a CUPED estimate.
Supports both call styles:
regression_assumptions_table_from_estimate(estimate, ...)
regression_assumptions_table_from_estimate(data, estimate, ...)
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.regression_assumptions_table_from_estimate
causalis.scenarios.cuped.diagnostics.regression_checks.regression_assumptions_table_from_dataFit CUPED on CausalData and return the assumptions flag table.
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.regression_assumptions_table_from_data
causalis.scenarios.cuped.diagnostics.regression_checks.overall_assumption_flagReturn overall GREEN/YELLOW/RED status from an assumptions table.
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.overall_assumption_flag
causalis.scenarios.cuped.diagnostics.regression_checks.style_regression_assumptions_tableReturn pandas Styler with colored flag cells for notebook display.
Canonical target
causalis.scenarios.cuped.diagnostics.regression_checks.style_regression_assumptions_table