Unleashing GPU Power: Accelerating Causal Inference for Big Data Analytics

GPU Acceleration for Causal Inference

With the continuous growth of data generated by consumer applications, enterprises are increasingly adopting causal inference methods to analyze observational data. Over the past decade, double machine learning has emerged as a technique that combines machine learning models with causal inference problems.

However, processing large datasets on CPUs has been challenging. NVIDIA RAPIDS, an open-source GPU-accelerated data science and AI library, includes cuML which is a machine learning library for Python and compatible with scikit-learn. By integrating RAPIDS cuML with the DoubleML library, data scientists can achieve faster causal inference, effectively handling large datasets.

This technology can provide up to a 12x speedup compared to CPU-based methods with minimal code adjustments.

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