Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies
通过条件独立关系以多项式延迟检验含隐变量的因果模型
Testing a hypothesized causal model against observational data is a key prerequisite for many causal inference tasks. A natural approach is to test whether the conditional independence relations (CIs) assumed in the model hold in the data. While a model can assume exponentially many CIs (with respect to the number of variables), testing all of them is both impractical and unnecessary. Causal graphs, which encode these CIs in polynomial space, give rise to local Markov…