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生成式暴露映射模型中非对称关系的量化与交叉拟合推断AI 解读

Learning directionality between variables is crucial yet challenging, especially for mechanistic relationships without a priori ordering assumptions. We propose a coefficient of asymmetry to quantify directional asymmetry using Shannon's entropy within a generative exposure mapping (GEM) framework. GEMs arise from experiments where a generative function $g$ maps exposure $X$ to outcome $Y$ through $Y = g(X)$, extended to noise-perturbed GEMs as $Y = g(X) + ε$. Our approach…

协变量调整的二元因果发现框架AI 解读

Ascertaining causal direction from observational data is a fundamental challenge in scientific inquiry. Of particular interest is the problem of covariate-adjusted bivariate causal discovery, i.e., determining the causal direction between X and Y in the presence of Z. While unadjusted bivariate causal discovery has seen significant advances (Hoyer et al., 2008; Ni, 2022), there is a lack of methodology dealing with real-world bivariate relationships, which are often…

因果发现中的预测不对称性：信息论方法AI 解读

Causal investigations in observational studies pose a great challenge in research where randomized trials or intervention-based studies are not feasible. We develop an information geometric causal discovery and inference framework of "predictive asymmetry". For $(X, Y)$, predictive asymmetry enables assessment of whether $X$ is more likely to cause $Y$ or vice-versa. The asymmetry between cause and effect becomes particularly simple if $X$ and $Y$ are deterministically…