In recommendation scenarios, there are two long-standing challenges, i.e., selection bias and data sparsity, which lead to a significant drop in prediction accuracy for both Click-Through Rate (CTR) and post-click Conversion Rate (CVR) tasks. To cope with these issues, existing works emphasize on leveraging Multi-Task Learning (MTL) frameworks (Category 1) or causal debiasing frameworks (Category 2) to incorporate more auxiliary data in the entire exposure/inference space D or debias the selection bias in the click/training space O. However, these two kinds of solutions cannot effectively address the not-missing-at-random problem and debias the selection bias in O to fit the inference in D. To fill the research gaps, we propose a Direct entire-space Causal Multi-Task framework, namely DCMT, for post-click conversion prediction in this paper. Specifically, inspired by users' decision process of conversion, we propose a new counterfactual mechanism to debias the selection bias in D, which can predict the factual CVR and the counterfactual CVR under the soft constraint of a counterfactual prior knowledge. Extensive experiments demonstrate that our DCMT can improve the state-of-the-art methods by an average of 1.07% in terms of CVR AUC on the five offline datasets and 0.75% in terms of PV-CVR on the online A/B test (the Alipay Search). Such improvements can increase millions of conversions per week in real industrial applications, e.g., the Alipay Search.

翻译：在推荐场景中，长期存在两大挑战——选择偏差与数据稀疏性，这导致点击率（CTR）和点击后转化率（CVR）任务的预测精度显著下降。为解决这些问题，现有工作主要采用多任务学习框架（类别1）或因果去偏框架（类别2），通过在全局曝光/推断空间D中引入更多辅助数据，或在点击/训练空间O中消除选择偏差。然而，这两类方法均无法有效处理非随机缺失问题，也无法修正O中的选择偏差以适应D中的推断需求。为填补研究空白，本文提出一种名为DCMT的全局空间因果多任务框架，用于点击后转化预测。具体而言，受用户转化决策过程启发，我们设计了一种新的反事实机制来消除D中的选择偏差，该机制可在反事实先验知识的软约束下，同时预测事实CVR与反事实CVR。大量实验表明，在五个离线数据集的CVR AUC指标上，DCMT方法较现有最优方法平均提升1.07%；在线上A/B测试（支付宝搜索场景）的PV-CVR指标上，提升幅度达0.75%。这种改进在支付宝搜索等实际工业应用中，每周可增加数百万次转化。