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The increasing need for causal analysis in large-scale industrial datasetsnecessitates the development of efficient and scalable causal algorithms forreal-world applications. This paper addresses the challenge of scaling causalalgorithms in the context of conducting causal analysis on extensive datasetscommonly encountered in industrial settings. Our proposed solution involvesenhancing the scalability of causal algorithm libraries, such as EconML, byleveraging the parallelism capabilities offered by the distributed computingframework Ray. We explore the potential of parallelizing key iterative stepswithin causal algorithms to significantly reduce overall runtime, supported bya case study that examines the impact on estimation times and costs. Throughthis approach, we aim to provide a more effective solution for implementingcausal analysis in large-scale industrial applications.

Accelerating Causal Algorithms for Industrial-scale Data: A Distributed Computing Approach with Ray Framework