PAIR-CI: Calibrated Conditional Independence Testing for Causal Discovery with Incomplete Data
The standard constraint-based paradigm for causal discovery with incomplete data -- impute first, test second -- is frequently miscalibrated: any consistent conditional independence (CI) test rejects a true null with probability approaching 1 when imputation error induces spurious conditional dependence. We introduce PAIR-CI, a nonparametric CI test that restores calibration by integrating multiple imputation directly into the inferential procedure via a paired permutation design. PAIR-CI compares cross-validated models that include and exclude the candidate variable while receiving the same imputed conditioning set, forcing imputation error to cancel in their loss difference rather than ...
| Authors | Thomas S. Robinson, Ranjit Lall |
|---|---|
| Primary category | stat.ME |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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