B1189
Title: Conformal-based hypothesis testing using rank sum statistics
Authors: Jing Lei - Carnegie Mellon University (United States) [presenting]
Abstract: The problem of testing the equality of the conditional distribution is considered for a response variable given a set of covariates between two populations. Such a testing problem is related to transfer learning and causal inference. We develop a nonparametric procedure by combining recent advances in conformal prediction with some new ingredients, such as a novel choice of conformity score and data-driven choices of weight and score functions. To our knowledge, this is the first successful attempt to use conformal prediction for testing statistical hypotheses beyond exchangeability. Our method is suitable for modern machine learning scenarios where the data has high dimensionality and large sample sizes, and can be effectively combined with existing classification algorithms to find good weight and score functions. The performance of the proposed method is demonstrated in synthetic and real data examples.
