  Unbiased learning to rank (ULTR) studies the problem of mitigating various
biases from implicit user feedback data such as clicks, and has been receiving
considerable attention recently. A popular ULTR approach for real-world
applications uses a two-tower architecture, where click modeling is factorized
into a relevance tower with regular input features, and a bias tower with
bias-relevant inputs such as the position of a document. A successful
factorization will allow the relevance tower to be exempt from biases. In this
work, we identify a critical issue that existing ULTR methods ignored - the
bias tower can be confounded with the relevance tower via the underlying true
relevance. In particular, the positions were determined by the logging policy,
i.e., the previous production model, which would possess relevance information.
We give both theoretical analysis and empirical results to show the negative
effects on relevance tower due to such a correlation. We then propose three
methods to mitigate the negative confounding effects by better disentangling
relevance and bias. Empirical results on both controlled public datasets and a
large-scale industry dataset show the effectiveness of the proposed approaches.
