Multiple imputation and test-wise deletion for causal discovery with incomplete cohort data
Causal discovery algorithms estimate causal graphs from observational data. This can provide a valuable complement to analyses focussing on the causal relation between individual treatment-outcome pairs. Constraint-based causal discovery algorithms rely on conditional independence testing when building the graph. Until recently, these algorithms have been unable to handle missing values. In this paper, we investigate two alternative solutions: Test-wise deletion and multiple imputation. We establish necessary and sufficient conditions for the recoverability of causal structures under test-wise deletion, and argue that multiple imputation is more challenging in the context of causal discovery than for estimation. We conduct an extensive comparison by simulating from benchmark causal graphs: As one might expect, we find that test-wise deletion and multiple imputation both clearly outperform list-wise deletion and single imputation. Crucially, our results further suggest that multiple imputation is especially useful in settings with a small number of either Gaussian or discrete variables, but when the dataset contains a mix of both neither method is uniformly best. The methods we compare include random forest imputation and a hybrid procedure combining test-wise deletion and multiple imputation. An application to data from the IDEFICS cohort study on diet- and lifestyle-related diseases in European children serves as an illustrating example.
Code (1)
Tasks
Causal DiscoveryImputationMissing ValuesSimilar Papers 제목 키워드 기반
Fast Causal Inference with Non-Random Missingness by Test-Wise Deletion
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. Li…
Causal DiscoveryCausal InferenceImputationMissing ValuesThe influence of missing data mechanisms and simple missing data handling techniques on fairness
Fairness of machine learning algorithms is receiving increasing attention, as such algorithms permeate the day-to-day aspects of our lives. One way in which bias can manifest in a dataset is through missing values. If da…
FairnessImputationMissing ValuesGene Regulatory Network Inference in the Presence of Dropouts: a Causal View
Gene regulatory network inference (GRNI) is a challenging problem, particularly owing to the presence of zeros in single-cell RNA sequencing data: some are biological zeros representing no gene expression, while some oth…
ImputationPAIR-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 pro…
E2GAN: End-to-End Generative Adversarial Network or Multivariate Time Series Imputation
The missing values, appear in most of multivariate time series, prevent advanced analysis of multivariate time series data. Existing imputation approaches try to deal with missing values by deletion, statistical imputati…
Generative Adversarial NetworkImputationMissing ValuesMultivariate Time Series Imputation+2