iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models
NeurIPS(2023)
摘要
Structural causal models (SCMs) are widely used in various disciplines to
represent causal relationships among variables in complex systems.
Unfortunately, the underlying causal structure is often unknown, and estimating
it from data remains a challenging task. In many situations, however, the end
goal is to localize the changes (shifts) in the causal mechanisms between
related datasets instead of learning the full causal structure of the
individual datasets. Some applications include root cause analysis, analyzing
gene regulatory network structure changes between healthy and cancerous
individuals, or explaining distribution shifts. This paper focuses on
identifying the causal mechanism shifts in two or more related datasets over
the same set of variables – without estimating the entire DAG structure of
each SCM. Prior work under this setting assumed linear models with Gaussian
noises; instead, in this work we assume that each SCM belongs to the more
general class of nonlinear additive noise models (ANMs). A key technical
contribution of this work is to show that the Jacobian of the score function
for the mixture distribution allows for the identification of shifts under
general non-parametric functional mechanisms. Once the shifted variables are
identified, we leverage recent work to estimate the structural differences, if
any, for the shifted variables. Experiments on synthetic and real-world data
are provided to showcase the applicability of this approach. Code implementing
the proposed method is open-source and publicly available at
https://github.com/kevinsbello/iSCAN.
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关键词
causal mechanism shifts,noise models,nonlinear additive
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