TSCAN: Context-Aware Uplift Modeling via Two-Stage Training for Online Merchant Business Diagnosis

Abstract: A primary challenge in ITE estimation is sample selection bias. Traditional approaches utilize treatment regularization techniques such as the Integral Probability Metrics (IPM), re-weighting, and propensity score modeling to mitigate this bias. However, these regularizations may introduce undesirable information loss and limit the performance of the model. Furthermore, treatment effects vary across different external contexts, and the existing methods are insufficient in fully interacting with and utilizing these contextual features. To address these issues, we propose a Context-Aware uplift model based on the Two-Stage training approach (TSCAN), comprising CAN-U and CAN-D sub-models. In the first stage, we train an uplift model, called CAN-U, which includes the treatment regularizations of IPM and propensity score prediction, to generate a complete dataset with counterfactual uplift labels. In the second stage, we train a model named CAN-D, which utilizes an isotonic output layer to directly model uplift effects, thereby eliminating the reliance on the regularization components. CAN-D adaptively corrects the errors estimated by CAN-U through reinforcing the factual samples, while avoiding the negative impacts associated with the aforementioned regularizations. Additionally, we introduce a Context-Aware Attention Layer throughout the two-stage process to manage the interactions between treatment, merchant, and contextual features, thereby modeling the varying treatment effect in different contexts. We conduct extensive experiments on two real-world datasets to validate the effectiveness of TSCAN. Ultimately, the deployment of our model for real-world merchant diagnosis on one of China's largest online food ordering platforms validates its practical utility and impact.

Context-Aware Uplift Modeling: TSCAN's Two-Stage Approach

The paper "TSCAN: Context-Aware Uplift Modeling via Two-Stage Training for Online Merchant Business Diagnosis," authored by Hangtao Zhang, Zhe Li, and Kairui Zhang, presents a novel approach to uplift modeling in the context of online food ordering industry. The primary focus of this work is to address the challenges of estimating Individual Treatment Effect (ITE) with special attention to sample selection bias and contextual interaction in treatment effects.

Overview of TSCAN Methodology

TSCAN introduces a two-stage training approach, which is a significant deviation from existing uplift modeling techniques. Traditional models for measuring ITE, including popular meta-learning and deep learning methods like BART and CausalForest, often address sample selection bias using forms of regularization such as Integral Probability Metrics (IPM) and propensity score modeling. However, such methods are observed to introduce information loss detrimental to model performance, particularly when contextual treatment effects are inadequately leveraged.

The paper proposes two sub-models within TSCAN: CAN-U and CAN-D, each contributing to a distinct stage of the training strategy. The initial CAN-U model applies IPM loss and propensity score prediction to generate a dataset complete with counterfactual uplift labels, which is then leveraged in the second stage with the CAN-D model. This subsequent model utilizes an isotonic output layer, ensuring the causal uplift effects are directly modeled without relying on regularization components that often impact the generalization performance negatively.

The integration of a Context-Aware Attention Layer throughout both stages further distinguishes TSCAN, allowing dynamic interaction between treatments, merchants, and contextual features. This advances the model's capability in adapting to variable treatment effects in diverse contexts, which is critical for practical applications like merchant diagnosis on large online platforms.

Experimental Validation and Implications

Extensive experiments on two real-world datasets, Eleshop-1M and Shop Activities, reveal TSCAN's effectiveness. Notably, the proposed model surpasses baseline methods in metrics such as normalized QINI and AUUC, particularly highlighting improvements in contextual prediction accuracy captured by CAUUC and CQINI metrics. Such findings corroborate TSCAN's capacity in better estimating causal effects, reinforcing its utility in operational settings geared towards enhancing merchant strategies.

The practical deployment of TSCAN in merchant diagnostics via Ele.me platform demonstrates tangible improvements in order volume when applying model-generated strategic decisions, indicating its substantial impact on real-world e-commerce environments.

Future Directions

This work opens avenues for further exploration in enhancing uplift modeling for various business scenarios. Future research could explore end-to-end training methodologies and extend context-aware interactions to harness more intricate features inherent in diverse business ecosystems. Developing architectures that allow more flexible adaptation across different data distributions and operational constraints in commercial settings would be a strategic focus for continued advancements in this domain.

In summary, the introduction of TSCAN represents a significant methodological advancement in the field of uplift modeling, offering robust solutions to sample bias and contextual interactions in treatment effect estimation. The implications for AI application in business diagnostics are profound, bridging theoretical innovation with practical utility in rapidly evolving digital and e-commerce landscapes.