Existing causal discovery methods based on combinatorial optimization or search are slow, prohibiting their application on large-scale datasets. In response, more recent methods attempt to address this limitation by formulating causal discovery as structure learning with continuous optimization but such approaches thus far provide no statistical guarantees. In this paper, we show that by efficiently parallelizing existing causal discovery methods, we can in fact scale them to thousands of dimensions, making them practical for substantially larger-scale problems. In particular, we parallelize the LiNGAM method, which is quadratic in the number of variables, obtaining up to a 32-fold speed-up on benchmark datasets when compared with existing sequential implementations. Specifically, we focus on the causal ordering subprocedure in DirectLiNGAM and implement GPU kernels to accelerate it. This allows us to apply DirectLiNGAM to causal inference on large-scale gene expression data with genetic interventions yielding competitive results compared with specialized continuous optimization methods, and Var-LiNGAM for causal discovery on U.S. stock data.

翻译：基于组合优化或搜索的现有因果发现方法速度较慢，难以应用于大规模数据集。为此，近期方法尝试将因果发现表述为基于连续优化的结构学习，但至今尚未提供统计保证。本文表明，通过高效并行化现有因果发现方法，我们实际上可将其扩展到数千维度，使其适用于更大规模问题。具体而言，我们并行化了LiNGAM方法（该方法计算复杂度与变量数呈二次关系），在基准数据集上相较于现有串行实现获得了高达32倍的加速比。我们重点针对DirectLiNGAM中的因果排序子过程，实现GPU内核进行加速。这使得我们能将DirectLiNGAM应用于含遗传干预的大规模基因表达数据因果推断，与专用连续优化方法相比展现出竞争力；同时将Var-LiNGAM应用于美国股票数据的因果发现。