CauImputation: Utilizing Structural Causal Model in Data Imputation

Missing data pose significant obstacles in data analysis. Many imputation methods, operating under the assumption that similar instances exhibit similar feature values, often overlook the essential role of the feature containing the missing value. These methods tend to apply a uniform strategy to all missing data within a single instance. This study proposes selecting neighbor instances for imputation based on both instance and missing feature information. To support this approach, a Structural Causal Model (SCM) delineates the relationships among missing data, features, and instances. The model reveals that the feature with the target missing value acts as a confounder, influencing both the selection of neighbor instances and the prediction of the missing value. Building upon these findings, this study introduces a new imputation method called the Causal Imputation strategy (CauImputation). CauImputation utilizes both feature and instance information to select neighbor instances and then applies causal interventions to compute the missing value. This strategy effectively leverages feature information while mitigating confounding effects. Tests on the National Alzheimer’s Coordinating Center (NACC) dataset and the National Center for Biotechnology Information (NCBI) microarray datasets demonstrate that CauImputation outperforms nine established methods, achieving a 3% to 15% increase in imputation accuracy and reducing root mean squared errors by 0.05 to 0.2. By utilizing the Structural Causal Model, this research introduces a novel perspective on data imputation, enhancing the robustness and reliability of biomedical data analyses.