In fields such as finance, climate science, and neuroscience, inferring
causal relationships from time series data poses a formidable challenge. While
contemporary techniques can handle nonlinear relationships between variables
and flexible noise distributions, they rely on the simplifying assumption that
data originates from the same underlying causal model. In this work, we relax
this assumption and perform causal discovery from time series data originating
from mixtures of different causal models. We infer both the underlying
structural causal models and the posterior probability for each sample
belonging to a specific mixture component. Our approach employs an end-to-end
training process that maximizes an evidence-lower bound for data likelihood.
Through extensive experimentation on both synthetic and real-world datasets, we
demonstrate that our method surpasses state-of-the-art benchmarks in causal
discovery tasks, particularly when the data emanates from diverse underlying
causal graphs. Theoretically, we prove the identifiability of such a model
under some mild assumptions.

从混合不同因果模型的时间序列数据中，利用证据下界最大化的方法进行因果发现，经过实验证明在探测来自不同因果图的数据时，该方法优于现有基准模型。