标题： 因果图在多个环境下的可识别性 显示英文标题

标题： On the identifiability of causal graphs with multiple environments

摘要： 从独立同分布的观测数据中进行因果发现通常被认为是病态的。我们证明，如果我们能够获得结构因果模型的分布，并且还拥有来自仅两个在噪声统计上足够不同的环境的额外数据，则可以唯一地识别因果图。值得注意的是，这是文献中第一个保证在固定数量的环境中以及任意非线性机制下恢复整个因果图的结果。我们的唯一约束是噪声项的高斯性；然而，我们提出了可能的方法来放松这一要求。单独来看，我们扩展了独立成分分析（ICA）与因果发现之间众所周知的对偶性；最近的研究表明，可以从多个环境中解决非线性ICA，至少与源的数量一样多：我们证明，在只需访问更少辅助信息的情况下，同样可以实现因果发现。 显示英文摘要

摘要： Causal discovery from i.i.d. observational data is known to be generally ill-posed. We demonstrate that if we have access to the distribution of a structural causal model, and additional data from only two environments that sufficiently differ in the noise statistics, the unique causal graph is identifiable. Notably, this is the first result in the literature that guarantees the entire causal graph recovery with a constant number of environments and arbitrary nonlinear mechanisms. Our only constraint is the Gaussianity of the noise terms; however, we propose potential ways to relax this requirement. Of interest on its own, we expand on the well-known duality between independent component analysis (ICA) and causal discovery; recent advancements have shown that nonlinear ICA can be solved from multiple environments, at least as many as the number of sources: we show that the same can be achieved for causal discovery while having access to much less auxiliary information.