PAIR-CI restores calibration to conditional independence testing under missing data by using paired permutations that force imputation error to cancel in the loss difference, together with a consistent variance estimator that unifies cross-validation and imputation uncertainty.
Journal of the American Statistical Association , volume=
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The work gives conditions favoring complete-case over IPW estimators in federated settings with missing data and introduces a multi-model calibrated weighting estimator that is consistent when at least one candidate model is correct at each site.
citing papers explorer
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PAIR-CI: Calibrated Conditional Independence Testing for Causal Discovery with Incomplete Data
PAIR-CI restores calibration to conditional independence testing under missing data by using paired permutations that force imputation error to cancel in the loss difference, together with a consistent variance estimator that unifies cross-validation and imputation uncertainty.
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Federated Learning with Incomplete Data: When to Use Complete Cases and When to Weight
The work gives conditions favoring complete-case over IPW estimators in federated settings with missing data and introduces a multi-model calibrated weighting estimator that is consistent when at least one candidate model is correct at each site.