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线性非高斯结构方程模型的拟合优度检验AI 解读

The field of causal discovery develops model selection methods to infer cause-effect relations among a set of random variables. For this purpose, different modelling assumptions have been proposed to render cause-effect relations identifiable. One prominent assumption is that the joint distribution of the observed variables follows a linear non-Gaussian structural equation model. In this paper, we develop novel goodness-of-fit tests that assess the validity of this…

通过留一变量法交叉验证因果发现AI 解读

We propose a new approach to falsify causal discovery algorithms without ground truth, which is based on testing the causal model on a variable pair excluded during learning the causal model. Specifically, given data on $X, Y, \boldsymbol{Z}=X, Y, Z_1,\dots,Z_k$, we apply the causal discovery algorithm separately to the 'leave-one-out' data sets $X, \boldsymbol{Z}$ and $Y, \boldsymbol{Z}$. We demonstrate that the two resulting causal models, in the form DAGs, ADMGs, CPDAGs…

存在不可观测混杂的线性非高斯因果模型发现AI 解读

We consider linear non-Gaussian structural equation models that involve latent confounding. In this setting, the causal structure is identifiable, but, in general, it is not possible to identify the specific causal effects. Instead, a finite number of different causal effects result in the same observational distribution. Most existing algorithms for identifying these causal effects use overcomplete independent component analysis (ICA), which often suffers from convergence…

平稳扩散过程中的条件独立性AI 解读

Stationary distributions of multivariate diffusion processes have recently been proposed as probabilistic models of causal systems in statistics and machine learning. Motivated by these developments, we study stationary multivariate diffusion processes with a sparsely structured drift. Our main result gives a characterization of the conditional independence relations that hold in a stationary distribution. The result draws on a graphical representation of the drift structure…