Causal Discovery with Stage Variables for Health Time Series
This addresses the challenge of causal inference in healthcare when data is limited within disease stages, offering a method to improve accuracy for medical applications, though it appears incremental as it builds on existing causal discovery techniques.
The paper tackled the problem of discovering causal relationships in health time series data where relationships change over disease stages, by proposing Causal Discovery with Stage Variables (CDSV), which reweights data using stage variables to improve accuracy; results showed CDSV discovered more causes with fewer false discoveries in simulations, had lower FDR in eICU, and found more clinically relevant causes in MIMIC-III.
Using observational data to learn causal relationships is essential when randomized experiments are not possible, such as in healthcare. Discovering causal relationships in time-series health data is even more challenging when relationships change over the course of a disease, such as medications that are most effective early on or for individuals with severe disease. Stage variables such as weeks of pregnancy, disease stages, or biomarkers like HbA1c, can influence what causal relationships are true for a patient. However, causal inference within each stage is often not possible due to limited amounts of data, and combining all data risks incorrect or missed inferences. To address this, we propose Causal Discovery with Stage Variables (CDSV), which uses stage variables to reweight data from multiple time-series while accounting for different causal relationships in each stage. In simulated data, CDSV discovers more causes with fewer false discoveries compared to baselines, in eICU it has a lower FDR than baselines, and in MIMIC-III it discovers more clinically relevant causes of high blood pressure.