论文标题
基于因果和机制的独立性，潜在的仪器变量是因果推断的先验
Latent Instrumental Variables as Priors in Causal Inference based on Independence of Cause and Mechanism
论文作者
论文摘要
基于条件独立性构造马尔可夫等效图的因果推理方法，不能应用于双变量情况。相反，基于原因和机理状态的独立性，可以推断出两种观察结果的因果发现。在我们的贡献中，我们挑战了这两个研究方向。我们研究了潜在变量，例如潜在仪器变量和因果图形结构中隐藏的常见原因。我们表明，基于原因和机制的独立性的方法间接包含隐藏仪器变量存在的痕迹。我们得出了一种新型算法来推断两个变量之间的因果关系，并在模拟数据和因果对的基准上验证了所提出的方法。我们通过实验说明，与最先进的方法相比，就经验准确性而言，所提出的方法非常简单且具有竞争力。
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 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 the 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.
