The increasing need for causal analysis in large-scale industrial datasets necessitates the development of efficient and scalable causal algorithms for real-world applications. This paper addresses the challenge of scaling causal algorithms in the context of conducting causal analysis on extensive datasets commonly encountered in industrial settings. Our proposed solution involves enhancing the scalability of causal algorithm libraries, such as EconML, by leveraging the parallelism capabilities offered by the distributed computing framework Ray. We explore the potential of parallelizing key iterative steps within causal algorithms to significantly reduce overall runtime, supported by a case study that examines the impact on estimation times and costs. Through this approach, we aim to provide a more effective solution for implementing causal analysis in large-scale industrial applications.

翻译：随着大规模工业数据集中因果分析需求的日益增长，开发高效且可扩展的因果算法以应对实际应用场景变得至关重要。本文旨在解决在对工业场景中常见的大规模数据集进行因果分析时，因果算法所面临的可扩展性挑战。我们提出的方案通过利用分布式计算框架Ray提供的并行能力，来增强EconML等因果算法库的可扩展性。我们探索了并行化因果算法内关键迭代步骤的潜力，以显著降低整体运行时间，并通过案例研究评估了该方法对估算时间与成本的影响。通过这一方案，我们旨在为大规模工业应用中实施因果分析提供更有效的解决方案。