  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.
