Deep causal learning for continuous testing, diagnosis, and optimization

This invention describes a system that automatically runs experiments within a larger system to figure out what causes certain outcomes. It repeatedly creates small, self-adjusting tests, injects them, measures the results, and then refines the tests to pinpoint the exact cause-and-effect relationships. This understanding can then be used to improve how the system works, particularly for managing complex sequences of operations or handling demands in a queue.