这篇论文给CVR因果估计出了个新招，双稳健加目标正则化，比简单去偏更靠谱，搞广告电商的可以看看。
本文提出一种基于半参数理论的双稳健CVR因果效应估计器，用于纠正点击样本选择偏差。该估计器相比干扰参数估计达到更快的收敛速度，对神经网络等灵活非参数估计更稳健。作者基于理论构建了目标正则化框架，以提升数值稳定性和实际可用性。在合成与真实数据上的实验表明方法有效，且简单组合损失去偏与标准因果估计器不如本方法。
Doubly Robust Estimation of Causal Effect on CVR with Targeted Regularization
Post-click conversion rate (CVR) is a key metric in various scenarios including e-commerce and advertising, reflecting the efficiency and user experience in the second stage of the conversion process. Estimating the causal effect on CVR is therefore of great practical importance. However, directly applying existing causal inference methods to clicked samples introduces sample selection bias and increased variance due to the exclusion of non-click data. Recent studies on CVR prediction introduce "ideal loss", which optimizes model parameters using an unbiased estimate of the loss over the full sample. Nevertheless, there is no guarantee that unbiasedness of the loss implies unbiasedness of the final estimator. We revisit this challenge from the perspective of semiparametric theory. Specifically, we develop a new doubly robust causal effect estimator for chain-structured outcomes such as CVR, and derive its theoretical properties in detail. It achieves a faster convergence rate compared to nuisance parameters estimation and is therefore more robust when using flexible nonparametric estimators, including neural networks. Based on these theoretical findings, we further design a framework based on targeted regularization to improve numerical stability and practical applicability. Extensive experiments on synthetic and real-world data demonstrate the effectiveness and robustness of our method. In addition, we find that naively combining loss debiasing with standard causal estimators underperforms our method, highlighting the necessity of developing the new estimator tailored to this CVR-style objective with solid theoretical guarantees.