Fast Causal Inference with Non-Random Missingness by Test-Wise Deletion
This addresses the issue of inefficient sample usage in causal discovery for datasets with missing not at random values, offering a practical improvement for researchers in fields like statistics and machine learning.
The paper tackles the problem of causal inference with non-random missing data by proposing test-wise deletion, which retains more samples than list-wise deletion while maintaining soundness, and shows it outperforms existing methods in synthetic and real data.
Many real datasets contain values missing not at random (MNAR). In this scenario, investigators often perform list-wise deletion, or delete samples with any missing values, before applying causal discovery algorithms. List-wise deletion is a sound and general strategy when paired with algorithms such as FCI and RFCI, but the deletion procedure also eliminates otherwise good samples that contain only a few missing values. In this report, we show that we can more efficiently utilize the observed values with test-wise deletion while still maintaining algorithmic soundness. Here, test-wise deletion refers to the process of list-wise deleting samples only among the variables required for each conditional independence (CI) test used in constraint-based searches. Test-wise deletion therefore often saves more samples than list-wise deletion for each CI test, especially when we have a sparse underlying graph. Our theoretical results show that test-wise deletion is sound under the justifiable assumption that none of the missingness mechanisms causally affect each other in the underlying causal graph. We also find that FCI and RFCI with test-wise deletion outperform their list-wise deletion and imputation counterparts on average when MNAR holds in both synthetic and real data.