Accelerate causal inference with NVIDIA RAPIDS and cuML

As the amount of data generated by consumer applications continues to increase, enterprises are increasingly adopting causal inference methods to analyze observational data. According to the NVIDIA blog, this approach provides insight into how changes to specific components affect key business metrics.

Advances in causal inference technology

Over the past decade, econometricians have developed a technique called dual machine learning, which integrates machine learning models into causal inference problems. This involves training two prediction models on independent samples of the data set and combining them to produce an unbiased estimate of the target variable. Open source Python libraries such as DoubleML facilitate this technique, although it faces challenges when processing large data sets on CPUs.

NVIDIA RAPIDS and the role of cuML

NVIDIA RAPIDS, a collection of open source GPU-accelerated data science and AI libraries, includes cuML, a machine learning library for Python that is compatible with scikit-learn. By leveraging RAPIDS cuML with the DoubleML library, data scientists can achieve faster causal inference and effectively process large datasets.

The integration of RAPIDS cuML allows companies to bridge the gap between prediction-driven innovation and real-world applications by leveraging computationally intensive machine learning algorithms for causal inference. This is especially useful when existing CPU-based methods struggle to meet the requirements of growing data sets.

Improved benchmarking performance

The performance of cuML was benchmarked against scikit-learn using different dataset sizes. Results show that on a dataset with 10 million rows and 100 columns, the CPU-based DoubleML pipeline took over 6.5 hours, but GPU-accelerated RAPIDS cuML reduced this time to just 51 minutes, achieving a 7.7x speedup.

These accelerated machine learning libraries can provide up to 12x speedup over CPU-based methods with minimal code tweaks. These substantial improvements highlight the potential of GPU acceleration in transforming data processing workflows.

conclusion

Causal inference plays a critical role in helping companies understand the impact of key product components. However, leveraging machine learning innovations for this purpose has historically been difficult. Technologies such as dual machine learning combined with accelerated computing libraries such as RAPIDS cuML enable companies to overcome these challenges by turning hours of processing time into minutes with minimal code changes.

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