Unified Causal Discovery and Missing Data Imputation

Osman Mian, Jens Kleesiek, Michael Kamp

TL;DR: We perform layer-wise ordered causal structure learning with missing data imputation

Abstract: Causal discovery and data imputation are often treated separately, yet both face challenges when data are missing. Existing causal discovery methods discard incomplete samples, losing valuable information, while standard imputation relies on spurious correlations that obscure the causal signal. We propose LOGIC, a framework that performs causal discovery and causally consistent imputation jointly. In contrast to prior work that assumes all source variables are observed, we derive a verifiable criterion for this assumption under MCAR and MAR missingness, grounded in the Algorithmic Markov Condition. LOGIC then proceeds layer by layer: identifying sources, recovering downstream relations, and imputing missing values, while explicitly declaring unknowns when imputation is unsupported. This design preserves causal reasoning even in challenging missingness regimes. Experiments on synthetic and real-world data show that LOGIC outperforms state-of-the-art baselines in both structure recovery and imputation accuracy.

Code Dataset Promise: Yes

Code Dataset Url: https://github.com/osman-mian/LoGIC

Format Confirmation: I agree that I have read and followed the formatting instructions for the camera ready version.

Submission Number: 958