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Distributional Balancing with Machine Learning for Clinical Trial Augmentation Using Real-World Data

arXiv · AI, language, vision and robotics · article · Sep 20, 2026 · UTC

In clinical trials, randomization of treatment and control groups is typically used to ensure the groups have similar covariate distributions on average, resulting in unbiased causal effect estimation. Such balanced covariate distributions are hard to achieve in practice, however, due to recruitment costs, patient dropouts, and more. One possible solution to this problem is to include control patients from external, real world databases. In this paper, we propose DBML, a method that selects control units from a real world database by matching the distribution between the treatment group and th

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