Counterfactual Learning to Rank using Heterogeneous Treatment Effect Estimation

1 code implementation • 19 Jul 2020 • Mucun Tian, Chun Guo, Vito Ostuni, Zhen Zhu

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.

counterfactual Learning-To-Rank +1