AI RESEARCH
PAIR-CI: Calibrated Conditional Independence Testing for Causal Discovery with Incomplete Data
arXiv CS.LG
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ArXi:2605.04838v1 Announce Type: cross 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