# CanniUplift: A Holistic Framework for Mitigating Seller and Incentive Cannibalization in E-commerce Uplift Modeling

arXiv: https://arxiv.org/abs/2607.05242 · HTML: https://mlsanity.com/paper/2607.05242/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 6 Jul 2026.

## Builds on (most cited first, 11 of 11)

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- [Mapping of CNNs on multi-core RRAM-based CIM architectures](https://mlsanity.com/paper/2309.03805/) (2023)
- [HiDP: Hierarchical DNN Partitioning for Distributed Inference on Heterogeneous Edge Platforms](https://mlsanity.com/paper/2411.16086/) (2024)
- [CIM-MLC: A Multi-level Compilation Stack for Computing-In-Memory Accelerators](https://mlsanity.com/paper/2401.12428/) (2024)
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- [Low-Rank Compression for IMC Arrays](https://mlsanity.com/paper/2502.07820/) (2025)
- [Mixed-Precision Training and Compilation for RRAM-based Computing-in-Memory Accelerators](https://mlsanity.com/paper/2601.21737/) (2026)

## Abstract (arXiv)

Personalized incentive allocation is vital for e-commerce, where uplift modeling is the standard for estimating Individual Treatment Effects (ITE). However, traditional models often fail in complex multi-seller environments with violations of the Stable Unit Treatment Value Assumption (SUTVA). We identify two critical challenges: Seller-level Cannibalization, where incentives shift expenditure between shops without growing the platform, and Incentive-level Cannibalization, where organic conversions or alternative rewards introduce significant noise into incrementality estimation. In this paper, we propose CanniUplift, a unified framework to mitigate these dual-source cannibalization effects. Specifically, we design Platform-level Global Alignment (PGA) to capture cross-shop substitution through global GMV consistency constraints. To tackle incentive-driven noise, we introduce Redemption-based Decomposition Denoising (RDD), which uses redemption behavior to decompose treated outcomes and reduce attribution noise within an entire-space framework. Furthermore, a Treat-Attention mechanism is designed to model intricate interactions between users' historical behaviors and current treatment options. Extensive experiments on both synthetic and large-scale industrial datasets demonstrate that CanniUplift significantly outperforms state-of-the-art baselines. Ablation studies confirm that the integration of PGA and RDD consistently improves wAUUC and wQINI. Successfully deployed online, our framework achieved a 4.08% relative increase in platform-wide incremental GMV (Delta GMV) over the production baseline and improved ROI in online A/B tests, proving effective in driving global platform growth.