# DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimation

arXiv: https://arxiv.org/abs/2302.06141 · HTML: https://mlsanity.com/paper/2302.06141/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2023.

## Built on by (most cited first, 12 of 12)

- [Entire-Space Variational Information Exploitation for Post-Click Conversion Rate Prediction](https://mlsanity.com/paper/2502.15687/) (2024)
- [Practical Multi-Task Learning for Rare Conversions in Ad Tech](https://mlsanity.com/paper/2507.20161/) (2025)
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- [A Predict-Then-Optimize Customer Allocation Framework for Online Fund Recommendation](https://mlsanity.com/paper/2503.03165/) (2025)
- [On Predicting Post-Click Conversion Rate via Counterfactual Inference](https://mlsanity.com/paper/2510.04816/) (2025)
- [A Probabilistic Framework for Temporal Distribution Generalization in Industry-Scale Recommender Systems](https://mlsanity.com/paper/2511.21032/) (2025)
- [Conversion rate prediction in online advertising: modeling techniques, performance evaluation and future directions](https://mlsanity.com/paper/2512.01171/) (2025)
- [Cross-Head Attention Uplift Network with Inverse Propensity Score under Unobserved Confounding](https://mlsanity.com/paper/2606.27114/) (2026)
- [OrDA: Orthogonal Disentanglement of Access Habits Framework for Homepage Marketing Block Recommendations](https://mlsanity.com/paper/2607.13420/) (2026)
- [MARCO: Click-Intent Decomposition for Calibrated Ads Conversion Prediction](https://mlsanity.com/paper/2608.10562/) (2026)

## Abstract (arXiv)

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.