  Learning-to-Rank (LTR) models trained from implicit feedback (e.g. clicks)
suffer from inherent biases. A well-known one is the position bias -- documents
in top positions are more likely to receive clicks due in part to their
position advantages. To unbiasedly learn to rank, existing counterfactual
frameworks first estimate the propensity (probability) of missing clicks with
intervention data from a small portion of search traffic, and then use inverse
propensity score (IPS) to debias LTR algorithms on the whole data set. These
approaches often assume the propensity only depends on the position of the
document, which may cause high estimation variance in applications where the
search context (e.g. query, user) varies frequently. While context-dependent
propensity models reduce variance, accurate estimations may require
randomization or intervention on a large amount of traffic, which may not be
realistic in real-world systems, especially for long tail queries. In this
work, we employ heterogeneous treatment effect estimation techniques to
estimate position bias when intervention click data is limited. We then use
such estimations to debias the observed click distribution and re-draw a new
de-biased data set, which can be used for any LTR algorithms. We conduct
simulations with varying experiment conditions and show the effectiveness of
the proposed method in regimes with long tail queries and sparse clicks.
