Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming
Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming
复制标题
基于约束的因果发现：通过答案集编程解决冲突
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Matti Järvisalo
中科院分区：
文献类型：
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作者：
Antti Hyttinen;F. Eberhardt;Matti Järvisalo
最近的因果发现方法的基础上，布尔可满足性求解器开辟了新的机会，考虑搜索空间的因果模型的反馈周期和不可测的混杂因素。然而，现有的方法到目前为止还没有能够提供一个原则性的帐户，如何处理冲突的约束，所产生的统计变异性。在这里，我们提出了一种新的方法，保留了布尔约束求解的多功能性，尽管存在统计误差，但仍达到了很高的精度。我们开发了一个新的逻辑编码（中）依赖约束，这是非常适合的域，并允许更快地解决。我们表示这种编码的答案集编程（ASP），并适用于最先进的ASP求解器的优化任务。基于不同的理论动机，我们探索了各种方法来处理统计错误。我们的方法目前可扩展到具有多达七个观测变量的循环潜变量模型，并且在精度上优于现有的基于约束的方法。
Recent approaches to causal discovery based on Boolean satisfiability solvers have opened new opportunities to consider search spaces for causal models with both feedback cycles and unmeasured confounders. However, the available methods have so far not been able to provide a principled account of how to handle conflicting constraints that arise from statistical variability. Here we present a new approach that preserves the versatility of Boolean constraint solving and attains a high accuracy despite the presence of statistical errors. We develop a new logical encoding of (in)dependence constraints that is both well suited for the domain and allows for faster solving. We represent this encoding in Answer Set Programming (ASP), and apply a state-of-the-art ASP solver for the optimization task. Based on different theoretical motivations, we explore a variety of methods to handle statistical errors. Our approach currently scales to cyclic latent variable models with up to seven observed variables and outperforms the available constraint-based methods in accuracy.