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
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- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- LinkedLinked via arxiv author · 85%Jiayi Dan →
“Doubly Robust Estimation of Causal Effect on CVR with Targeted Regularization”
- LinkedLinked via arxiv author · 85%Haobo Li →
“Doubly Robust Estimation of Causal Effect on CVR with Targeted Regularization”
- LinkedLinked via arxiv author · 85%Lu Deng →
“Doubly Robust Estimation of Causal Effect on CVR with Targeted Regularization”
- LinkedLinked via arxiv author · 85%Zhiyong Wang →
“Doubly Robust Estimation of Causal Effect on CVR with Targeted Regularization”