Sequential Conditional Independence Testing with Machine Learning Models
This research develops methods to test sequential conditional independence using machine learning models by decomposing testing errors into null enlargement, approximation, and estimation error. It explores intermediate null hypotheses to create robust 'e-variables' that are powerful under the alternative hypothesis, offering a data-driven approach for sequential testing.