Causal inference methods based on conditional independence construct Markov equivalent graphs and cannot be applied to bivariate cases. The approaches based on independence of cause and mechanism state, on the contrary, that causal discovery can be inferred for two observations. In our contribution, we pose a challenge to reconcile these two research directions. We study the role of latent variables such as latent instrumental variables and hidden common causes in the causal graphical structures. We show that methods based on the independence of cause and mechanism indirectly contain traces of the existence of the hidden instrumental variables. We derive a novel algorithm to infer causal relationships between two variables, and we validate the proposed method on simulated data and on a benchmark of cause-effect pairs. We illustrate by our experiments that the proposed approach is simple and extremely competitive in terms of empirical accuracy compared to the state-of-the-art methods.

中文翻译：

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工具变量在基于因果和机制独立的因果推理中的作用

基于条件独立的因果推理方法构造马尔可夫等价图，不能应用于二元情况。相反，基于原因和机制独立性的方法可以从两个观察中推断出因果发现。在我们的贡献中，我们提出了协调这两个研究方向的挑战。我们研究了潜在变量（例如潜在工具变量和隐藏的常见原因）在因果图形结构中的作用。我们表明基于原因和机制独立性的方法间接包含隐藏工具变量存在的痕迹。我们推导出一种新算法来推断两个变量之间的因果关系，并且我们在模拟数据和因果对基准上验证了所提出的方法。