The Estimation of Continual Causal Effect for Dataset Shifting Streams
- URL: http://arxiv.org/abs/2504.20471v1
- Date: Tue, 29 Apr 2025 07:13:28 GMT
- Title: The Estimation of Continual Causal Effect for Dataset Shifting Streams
- Authors: Baining Chen, Yiming Zhang, Yuqiao Han, Ruyue Zhang, Ruihuan Du, Zhishuo Zhou, Zhengdan Zhu, Xun Liu, Jiecheng Guo,
- Abstract summary: This paper focuses on capturing the dataset shift from user behavior and domain distribution changing over time.<n>We propose an Incremental Causal Effect with Proxy Knowledge Distillation (ICE-PKD) framework to tackle this challenge.<n>The ICE-PKD framework has been deployed in the marketing system of Huaxiaozhu, a ride-hailing platform in China.
- Score: 5.348397171353176
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- Review, Refine, Repeat: Understanding Iterative Decoding of AI Agents with Dynamic Evaluation and Selection [71.92083784393418]
Inference-time methods such as Best-of-N (BON) sampling offer a simple yet effective alternative to improve performance.<n>We propose Iterative Agent Decoding (IAD) which combines iterative refinement with dynamic candidate evaluation and selection guided by a verifier.
arXiv Detail & Related papers (2025-04-02T17:40:47Z) - Online-BLS: An Accurate and Efficient Online Broad Learning System for Data Stream Classification [52.251569042852815]
We introduce an online broad learning system framework with closed-form solutions for each online update.<n>We design an effective weight estimation algorithm and an efficient online updating strategy.<n>Our framework is naturally extended to data stream scenarios with concept drift and exceeds state-of-the-art baselines.
arXiv Detail & Related papers (2025-01-28T13:21:59Z) - Online Relational Inference for Evolving Multi-agent Interacting Systems [14.275434303742328]
Online Inference (ORI) is designed to efficiently identify hidden interaction graphs in evolving multi-agent interacting systems.
Unlike traditional offline methods that rely on a fixed training set, ORI employs online backpropagation, updating the model with each new data point.
A key innovation is the use of an adjacency matrix as a trainable parameter, optimized through a new adaptive learning technique called AdaRelation.
arXiv Detail & Related papers (2024-11-03T05:43:55Z) - Efficient Diffusion as Low Light Enhancer [63.789138528062225]
Reflectance-Aware Trajectory Refinement (RATR) is a simple yet effective module to refine the teacher trajectory using the reflectance component of images.
textbfReflectance-aware textbfDiffusion with textbfDistilled textbfTrajectory (textbfReDDiT) is an efficient and flexible distillation framework tailored for Low-Light Image Enhancement (LLIE)
arXiv Detail & Related papers (2024-10-16T08:07:18Z) - Online Spatio-Temporal Correlation-Based Federated Learning for Traffic
Flow Forecasting [11.253575460227127]
In this paper, we perform the first study of forecasting traffic flow adopting Online Learning (OL) manner in FL framework.
We then propose a novel prediction method named Online Spatio-Temporal Correlation-based Federated Learning (FedOSTC) to guarantee performance gains regardless of traffic fluctuation.
arXiv Detail & Related papers (2023-02-17T02:37:36Z) - Concept Drift Adaptation for CTR Prediction in Online Advertising
Systems [6.900209851954917]
Click-through rate (CTR) prediction is a crucial task in web search, recommender systems, and online advertisement displaying.
In this paper, we propose adaptive mixture of experts (AdaMoE) to alleviate the concept drift problem by adaptive filtering in the data stream of CTR prediction.
arXiv Detail & Related papers (2022-04-01T07:43:43Z) - Reinforcement Learning in the Wild: Scalable RL Dispatching Algorithm
Deployed in Ridehailing Marketplace [12.298997392937876]
This study proposes a real-time dispatching algorithm based on reinforcement learning.
It is deployed online in multiple cities under DiDi's operation for A/B testing and is launched in one of the major international markets.
The deployed algorithm shows over 1.3% improvement in total driver income from A/B testing.
arXiv Detail & Related papers (2022-02-10T16:07:17Z) - Data Augmentation through Expert-guided Symmetry Detection to Improve
Performance in Offline Reinforcement Learning [0.0]
offline estimation of the dynamical model of a Markov Decision Process (MDP) is a non-trivial task.
Recent works showed that an expert-guided pipeline relying on Density Estimation methods effectively detects this structure in deterministic environments.
We show that the former results lead to a performance improvement when solving the learned MDP and then applying the optimized policy in the real environment.
arXiv Detail & Related papers (2021-12-18T14:32:32Z) - Lifelong Unsupervised Domain Adaptive Person Re-identification with
Coordinated Anti-forgetting and Adaptation [127.6168183074427]
We propose a new task, Lifelong Unsupervised Domain Adaptive (LUDA) person ReID.
This is challenging because it requires the model to continuously adapt to unlabeled data of the target environments.
We design an effective scheme for this task, dubbed CLUDA-ReID, where the anti-forgetting is harmoniously coordinated with the adaptation.
arXiv Detail & Related papers (2021-12-13T13:19:45Z) - A Deep Value-network Based Approach for Multi-Driver Order Dispatching [55.36656442934531]
We propose a deep reinforcement learning based solution for order dispatching.
We conduct large scale online A/B tests on DiDi's ride-dispatching platform.
Results show that CVNet consistently outperforms other recently proposed dispatching methods.
arXiv Detail & Related papers (2021-06-08T16:27:04Z) - Extrapolation for Large-batch Training in Deep Learning [72.61259487233214]
We show that a host of variations can be covered in a unified framework that we propose.
We prove the convergence of this novel scheme and rigorously evaluate its empirical performance on ResNet, LSTM, and Transformer.
arXiv Detail & Related papers (2020-06-10T08:22:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.