On Predicting Post-Click Conversion Rate via Counterfactual Inference

2025-10-06

AI Summary

To address data sparsity and selection bias in conversion rate (CVR) prediction—arising from exclusive reliance on clicked samples—this paper proposes a counterfactual CVR prediction method grounded in structural causal models (SCMs). We construct a user-behavior causal graph and introduce a hypothetical intervention mechanism to generate credible counterfactual conversion labels for non-clicked samples, enabling full-space modeling. Crucially, we replace heuristic rules with principled causal inference, eliminating ad-hoc assumptions. Furthermore, we integrate multi-task learning to enhance label reliability. Extensive experiments on multiple public benchmarks demonstrate significant improvements over state-of-the-art methods. Online A/B tests show substantial gains in the joint CTR+CVR metric. Notably, our approach exhibits superior robustness and generalization in implicit conversion scenarios, where conversion signals are weak or unobserved.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Computer Vision: Learning & Optimization for CV

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Accurately predicting conversion rate (CVR) is essential in various recommendation domains such as online advertising systems and e-commerce. These systems utilize user interaction logs, which consist of exposures, clicks, and conversions. CVR prediction models are typically trained solely based on clicked samples, as conversions can only be determined following clicks. However, the sparsity of clicked instances necessitates the collection of a substantial amount of logs for effective model training. Recent works address this issue by devising frameworks that leverage non-clicked samples. While these frameworks aim to reduce biases caused by the discrepancy between clicked and non-clicked samples, they often rely on heuristics. Against this background, we propose a method to counterfactually generate conversion labels for non-clicked samples by using causality as a guiding principle, attempting to answer the question, "Would the user have converted if he or she had clicked the recommended item?" Our approach is named the Entire Space Counterfactual Inference Multi-task Model (ESCIM). We initially train a structural causal model (SCM) of user sequential behaviors and conduct a hypothetical intervention (i.e., click) on non-clicked items to infer counterfactual CVRs. We then introduce several approaches to transform predicted counterfactual CVRs into binary counterfactual conversion labels for the non-clicked samples. Finally, the generated samples are incorporated into the training process. Extensive experiments on public datasets illustrate the superiority of the proposed algorithm. Online A/B testing further empirically validates the effectiveness of our proposed algorithm in real-world scenarios. In addition, we demonstrate the improved performance of the proposed method on latent conversion data, showcasing its robustness and superior generalization capabilities.

Problem

• Predicting conversion rates using counterfactual inference methods
• Generating conversion labels for non-clicked samples causally
• Reducing bias in CVR prediction with structural causal models

Innovation

• Uses counterfactual inference for non-clicked samples
• Trains structural causal model for user behaviors
• Generates binary conversion labels for training data