A Causal Perspective to Unbiased Conversion Rate Estimation on Data Missing Not at Random
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In modern e-commerce and advertising recommender systems, ongoing research works attempt to optimize conversion rate (CVR) estimation, and increase the gross merchandise volume. Even though the state-of-the-art CVR estimators adopt deep learning methods, their model performances are still subject to sample selection bias and data sparsity issues. Conversion labels of exposed items in training dataset are typically missing not at random due to selection bias. Empirically, data sparsity issue causes the performance degradation of model with large parameter space. In this paper, we proposed two causal estimators combined with multi-task learning, and aim to solve sample selection bias (SSB) and data sparsity (DS) issues in conversion rate estimation. The proposed estimators adjust for the MNAR mechanism as if they are trained on a "do dataset" where users are forced to click on all exposed items. We evaluate the causal estimators with billion data samples. Experiment results demonstrate that the proposed CVR estimators outperform other state-of-the-art CVR estimators. In addition, empirical study shows that our methods are cost-effective with large scale dataset.
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