Post-click conversion rate (CVR) estimation is a fundamental task in developing effective recommender systems, yet it faces challenges from data sparsity and sample selection bias. To handle both challenges, the entire space multitask models are employed to decompose the user behavior track into a sequence of exposure $\rightarrow$ click $\rightarrow$ conversion, constructing surrogate learning tasks for CVR estimation. However, these methods suffer from two significant defects: (1) intrinsic estimation bias (IEB), where the CVR estimates are higher than the actual values; (2) false independence prior (FIP), where the causal relationship between clicks and subsequent conversions is potentially overlooked. To overcome these limitations, we develop a model-agnostic framework, namely Entire Space Counterfactual Multitask Model (ESCM$^2$), which incorporates a counterfactual risk minimizer within the ESMM framework to regularize CVR estimation. Experiments conducted on large-scale industrial recommendation datasets and an online industrial recommendation service demonstrate that ESCM$^2$ effectively mitigates IEB and FIP defects and substantially enhances recommendation performance.

翻译：点击后转化率（CVR）估计是构建高效推荐系统的基础任务，但面临数据稀疏性和样本选择偏差的双重挑战。为应对这两大挑战，全空间多任务模型通过将用户行为轨迹分解为“曝光→点击→转化”的序列，构建CVR估计的替代学习任务。然而，这类方法存在两个显著缺陷：（1）内在估计偏差（IEB），即CVR估计值高于实际值；（2）虚假独立性先验（FIP），即点击与后续转化之间的因果关系可能被忽略。为克服这些局限，我们开发了一种模型无关的框架——全空间反事实多任务模型（ESCM$^2$），该框架在ESMM框架中融入反事实风险最小化器以正则化CVR估计。在工业级推荐数据集和在线推荐服务上的实验表明，ESCM$^2$能有效缓解IEB和FIP缺陷，并显著提升推荐性能。