Mining genuine mechanisms underlying the complex data generation process in
real-world systems is a fundamental step in promoting interpretability of, and
thus trust in, data-driven models. Therefore, we propose a variation-based
cause effect identification (VCEI) framework for causal discovery in bivariate
systems from a single observational setting. Our framework relies on the
principle of independence of cause and mechanism (ICM) under the assumption of
an existing acyclic causal link, and offers a practical realization of this
principle. Principally, we artificially construct two settings in which the
marginal distributions of one covariate, claimed to be the cause, are
guaranteed to have non-negligible variations. This is achieved by re-weighting
samples of the marginal so that the resultant distribution is notably distinct
from this marginal according to some discrepancy measure. In the causal
direction, such variations are expected to have no impact on the effect
generation mechanism. Therefore, quantifying the impact of these variations on
the conditionals reveals the genuine causal direction. Moreover, we formulate
our approach in the kernel-based maximum mean discrepancy, lifting all
constraints on the data types of cause-and-effect covariates, and rendering
such artificial interventions a convex optimization problem. We provide a
series of experiments on real and synthetic data showing that VCEI is, in
principle, competitive to other cause effect identification frameworks.

通过 VCEI 框架，在单个观测设置中对双变量系统进行因果发现，基于因果和机制的独立性原则，人为构造两个设置，通过核最大平均偏差将人为干预转化为一个凸优化问题，通过一系列实验表明 VCEI 方法是一种有竞争力的因果发现方法。