# ESCM2: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation

arXiv: https://arxiv.org/abs/2204.05125 · HTML: https://mlsanity.com/paper/2204.05125/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 3 Apr 2022.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Recommendations as Treatments: Debiasing Learning and Evaluation](https://mlsanity.com/paper/1602.05352/) (2016)
- [Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://mlsanity.com/paper/1804.07931/) (2018)
- [Large-scale Causal Approaches to Debiasing Post-click Conversion Rate Estimation with Multi-task Learning](https://mlsanity.com/paper/1910.09337/) (2019)
- [Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising](https://mlsanity.com/paper/2105.08489/) (2021)
- [Enhanced Doubly Robust Learning for Debiasing Post-click Conversion Rate Estimation](https://mlsanity.com/paper/2105.13623/) (2021)
- [Top-N Recommendation with Counterfactual User Preference Simulation](https://mlsanity.com/paper/2109.02444/) (2021)
- [Recommender Systems with Heterogeneous Side Information](https://mlsanity.com/paper/1907.08679/) (2019)
- [An Analysis Of Entire Space Multi-Task Models For Post-Click Conversion Prediction](https://mlsanity.com/paper/2108.13475/) (2021)

## Built on by (most cited first, 30 of 47)

- [Optimal Transport for Treatment Effect Estimation](https://mlsanity.com/paper/2310.18286/) (2023)
- [STEM: Unleashing the Power of Embeddings for Multi-task Recommendation](https://mlsanity.com/paper/2308.13537/) (2023)
- [An Accurate and Interpretable Framework for Trustworthy Process Monitoring](https://mlsanity.com/paper/2302.10426/) (2023)
- [Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations](https://mlsanity.com/paper/2304.09085/) (2023)
- [Learnable Behavior Control: Breaking Atari Human World Records via Sample-Efficient Behavior Selection](https://mlsanity.com/paper/2305.05239/) (2023)
- [Entire Space Counterfactual Learning for Reliable Content Recommendations](https://mlsanity.com/paper/2210.11039/) (2022)
- [A Survey of Deep Causal Models and Their Industrial Applications](https://mlsanity.com/paper/2209.08860/) (2022)
- [Debiased Recommendation with Noisy Feedback](https://mlsanity.com/paper/2406.17182/) (2024)
- [Optimizing Airbnb Search Journey with Multi-task Learning](https://mlsanity.com/paper/2305.18431/) (2023)
- [Doubly Calibrated Estimator for Recommendation on Data Missing Not At Random](https://mlsanity.com/paper/2403.00817/) (2024)
- [Proximity Matters: Local Proximity Enhanced Balancing for Treatment Effect Estimation](https://mlsanity.com/paper/2407.01111/) (2024)
- [CausalRM: Causal-Theoretic Reward Modeling for RLHF from Observational User Feedbacks](https://mlsanity.com/paper/2603.18736/) (2026)
- [Debiased Collaborative Filtering with Kernel-Based Causal Balancing](https://mlsanity.com/paper/2404.19596/) (2024)
- [See Beyond a Single View: Multi-Attribution Learning Leads to Better Conversion Rate Prediction](https://mlsanity.com/paper/2508.15217/) (2025)
- [ADSNet: Cross-Domain LTV Prediction with an Adaptive Siamese Network in Advertising](https://mlsanity.com/paper/2406.10517/) (2024)
- [Modeling Task Relationships in Multi-variate Soft Sensor with Balanced Mixture-of-Experts](https://mlsanity.com/paper/2305.16360/) (2023)
- [ESMC: Entire Space Multi-Task Model for Post-Click Conversion Rate via Parameter Constraint](https://mlsanity.com/paper/2307.09193/) (2023)
- [ConvFormer: Revisiting Transformer for Sequential User Modeling](https://mlsanity.com/paper/2308.02925/) (2023)
- [Pareto Invariant Representation Learning for Multimedia Recommendation](https://mlsanity.com/paper/2308.04706/) (2023)
- [MEBS: Multi-task End-to-end Bid Shading for Multi-slot Display Advertising](https://mlsanity.com/paper/2403.02607/) (2024)
- [Rethinking the Diffusion Models for Numerical Tabular Data Imputation from the Perspective of Wasserstein Gradient Flow](https://mlsanity.com/paper/2406.15762/) (2024)
- [Entire-Space Variational Information Exploitation for Post-Click Conversion Rate Prediction](https://mlsanity.com/paper/2502.15687/) (2024)
- [Uncertainty Calibration for Counterfactual Propensity Estimation in Recommendation](https://mlsanity.com/paper/2303.12973/) (2023)
- [Deep Stable Multi-Interest Learning for Out-of-distribution Sequential Recommendation](https://mlsanity.com/paper/2304.05615/) (2023)
- [Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference](https://mlsanity.com/paper/2404.19620/) (2024)
- [Practical Multi-Task Learning for Rare Conversions in Ad Tech](https://mlsanity.com/paper/2507.20161/) (2025)
- [Modeling Cascaded Delay Feedback for Online Net Conversion Rate Prediction: Benchmark, Insights and Solutions](https://mlsanity.com/paper/2601.19965/) (2026)
- [Impression-Aware Recommender Systems](https://mlsanity.com/paper/2308.07857/) (2023)
- [The Importance of Causality in Decision Making: A Perspective on Recommender Systems](https://mlsanity.com/paper/2410.01822/) (2024)
- [Residual Multi-Task Learner for Applied Ranking](https://mlsanity.com/paper/2411.09705/) (2024)

## Abstract (arXiv)

Accurate estimation of post-click conversion rate is critical for building recommender systems, which has long been confronted with sample selection bias and data sparsity issues. Methods in the Entire Space Multi-task Model (ESMM) family leverage the sequential pattern of user actions, i.e. impressionrightarrow click rightarrow conversion to address data sparsity issue. However, they still fail to ensure the unbiasedness of CVR estimates. In this paper, we theoretically demonstrate that ESMM suffers from the following two problems: (1) Inherent Estimation Bias (IEB), where the estimated CVR of ESMM is inherently higher than the ground truth; (2) Potential Independence Priority (PIP) for CTCVR estimation, where there is a risk that the ESMM overlooks the causality from click to conversion. To this end, we devise a principled approach named Entire Space Counterfactual Multi-task Modelling (ESCM2), which employs a counterfactual risk miminizer as a regularizer in ESMM to address both IEB and PIP issues simultaneously. Extensive experiments on offline datasets and online environments demonstrate that our proposed ESCM2 can largely mitigate the inherent IEB and PIP issues and achieve better performance than baseline models.