mlsanityData to 14 Oct 2026

The Estimation of Continual Causal Effect for Dataset Shifting Streams

arXiv 2504.20471 ↗First posted 29 Apr 2025cs.LG cs.AI stat.MEcited by 0

On the map: Knowledge distillation → General knowledge distillation methods

Abstract (arXiv)

Causal effect estimation has been widely used in marketing optimization. The framework of an uplift model followed by a constrained optimization algorithm is popular in practice. To enhance performance in the online environment, the framework needs to be improved to address the complexities caused by temporal dataset shift. This paper focuses on capturing the dataset shift from user behavior and domain distribution changing over time. We propose an Incremental Causal Effect with Proxy Knowledge Distillation (ICE-PKD) framework to tackle this challenge. The ICE-PKD framework includes two components: (i) a multi-treatment uplift network that eliminates confounding bias using counterfactual regression; (ii) an incremental training strategy that adapts to the temporal dataset shift by updating with the latest data and protects generalization via replay-based knowledge distillation. We also revisit the uplift modeling metrics and introduce a novel metric for more precise online evaluation in multiple treatment scenarios. Extensive experiments on both simulated and online datasets show that the proposed framework achieves better performance. The ICE-PKD framework has been deployed in the marketing system of Huaxiaozhu, a ride-hailing platform in China.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in General knowledge distillation methods, one of 12 directions in Knowledge distillation. Builds on 23 of its 48 references that are in the map. First posted on arXiv 29 Apr 2025.

Builds on

23 of 48 references.

References · 23

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  2. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
  3. 2016Overcoming catastrophic forgetting in neural networkscited by 4,787
  4. 2016Learning without Forgettingcited by 2,496
  5. 2016iCaRL: Incremental Classifier and Representation Learningcited by 2,333
  6. 2020Knowledge Distillation: A Surveycited by 1,603
  7. 2018Efficient Lifelong Learning with A-GEMcited by 1,058
  8. 2018Overcoming catastrophic forgetting with hard attention to the taskcited by 681
  9. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 600
  10. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 478
  11. 2016Learning Representations for Counterfactual Inferencecited by 329
  12. 2019Adapting Neural Networks for the Estimation of Treatment Effectscited by 287
11 more
  1. 2019A continual learning survey: Defying forgetting in classification taskscited by 118
  2. 2020A Comprehensive Study of Class Incremental Learning Algorithms for Visual Taskscited by 107
  3. 2018Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilizationcited by 104
  4. 2023Explicit Feature Interaction-aware Uplift Network for Online Marketingcited by 25
  5. 2022Direct Heterogeneous Causal Learning for Resource Allocation Problems in Marketingcited by 21
  6. 2020Learning to rank for uplift modelingcited by 18
  7. 2022DESCN: Deep Entire Space Cross Networks for Individual Treatment Effect Estimationcited by 16
  8. 2023Continual Causal Inference with Incremental Observational Datacited by 13
  9. 2024Rankability-enhanced Revenue Uplift Modeling Framework for Online Marketingcited by 12
  10. 2023Causal Inference for Banking Finance and Insurance A Surveycited by 5
  11. 2019Causal Simulations for Uplift Modelingcited by 1

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  2. 2023KDSM: An uplift modeling framework based on knowledge distillation and sample matching
  3. 2024Multi-Treatment Multi-Task Uplift Modeling for Enhancing User Growth
  4. 2024De-confounded Data-free Knowledge Distillation for Handling Distribution Shifts
  5. 2023ShiftKD: Benchmarking Knowledge Distillation under Distribution Shift
  6. 2026CausalReasoningBenchmark: A Real-World Benchmark for Disentangled Evaluation of Causal Identification and Estimation
  7. 2026Three-in-One World Model: Energy-Based Consistency, Prediction, and Counterfactual Inference for Marketing Intervention
  8. 2023Causal normalizing flows: from theory to practice