Treatment Targeting by AUUC Maximization with Generalization Guarantees

• URL: http://arxiv.org/abs/2012.09897v1
• Date: Thu, 17 Dec 2020 19:32:35 GMT
• Title: Treatment Targeting by AUUC Maximization with Generalization Guarantees
• Authors: Artem Betlei, Eustache Diemert, Massih-Reza Amini
• Abstract summary: We consider the task of optimizing treatment assignment based on individual treatment effect prediction. We propose a generalization bound on the Area Under the Uplift Curve (AUUC) and present a novel learning algorithm that optimize a derivable surrogate of this bound, called AUUC-max.
• Score: 7.837855832568568
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider the task of optimizing treatment assignment based on individual treatment effect prediction. This task is found in many applications such as personalized medicine or targeted advertising and has gained a surge of interest in recent years under the name of Uplift Modeling. It consists in targeting treatment to the individuals for whom it would be the most beneficial. In real life scenarios, when we do not have access to ground-truth individual treatment effect, the capacity of models to do so is generally measured by the Area Under the Uplift Curve (AUUC), a metric that differs from the learning objectives of most of the Individual Treatment Effect (ITE) models. We argue that the learning of these models could inadvertently degrade AUUC and lead to suboptimal treatment assignment. To tackle this issue, we propose a generalization bound on the AUUC and present a novel learning algorithm that optimizes a derivable surrogate of this bound, called AUUC-max. Finally, we empirically demonstrate the tightness of this generalization bound, its effectiveness for hyper-parameter tuning and show the efficiency of the proposed algorithm compared to a wide range of competitive baselines on two classical benchmarks.

Related papers

• Unlearning as multi-task optimization: A normalized gradient difference approach with an adaptive learning rate [105.86576388991713]
  We introduce a normalized gradient difference (NGDiff) algorithm, enabling us to have better control over the trade-off between the objectives. We provide a theoretical analysis and empirically demonstrate the superior performance of NGDiff among state-of-the-art unlearning methods on the TOFU and MUSE datasets.
  arXiv  Detail & Related papers  (2024-10-29T14:41:44Z)
• Optimizing Treatment Allocation in the Presence of Interference [6.404584255185188]
  In Influence Maximization (IM), the objective is to select the optimal set of entities in a network to target with a treatment. In Uplift Modeling (UM), entities are ranked according to estimated treatment effect, and the top entities are allocated treatment. We show how a causal estimator is trained to predict treatment effects in a network setting and integrated into classic IM algorithms.
  arXiv  Detail & Related papers  (2024-09-30T15:48:22Z)
• Self-supervised Preference Optimization: Enhance Your Language Model with Preference Degree Awareness [27.43137305486112]
  We propose a novel Self-supervised Preference Optimization (SPO) framework, which constructs a self-supervised preference degree loss combined with the alignment loss. The results demonstrate that SPO can be seamlessly integrated with existing preference optimization methods to achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-09-26T12:37:26Z)
• Enhancing Robustness of Vision-Language Models through Orthogonality Learning and Self-Regularization [77.62516752323207]
  We introduce an orthogonal fine-tuning method for efficiently fine-tuning pretrained weights and enabling enhanced robustness and generalization. A self-regularization strategy is further exploited to maintain the stability in terms of zero-shot generalization of VLMs, dubbed OrthSR. For the first time, we revisit the CLIP and CoOp with our method to effectively improve the model on few-shot image classficiation scenario.
  arXiv  Detail & Related papers  (2024-07-11T10:35:53Z)
• Selective Mixup Fine-Tuning for Optimizing Non-Decomposable Objectives [17.10165955576643]
  Current state-of-the-art empirical techniques offer sub-optimal performance on practical, non-decomposable performance objectives. We propose SelMix, a selective mixup-based inexpensive fine-tuning technique for pre-trained models. We find that proposed SelMix fine-tuning significantly improves the performance for various practical non-decomposable objectives across benchmarks.
  arXiv  Detail & Related papers  (2024-03-27T06:55:23Z)
• Stage-Aware Learning for Dynamic Treatments [3.6923632650826486]
  We propose a novel individualized learning method for dynamic treatment regimes. By relaxing the restriction that the observed trajectory must be fully aligned with the optimal treatments, our approach substantially improves the sample efficiency and stability of IPWE-based methods.
  arXiv  Detail & Related papers  (2023-10-30T06:35:31Z)
• Joint Training of Deep Ensembles Fails Due to Learner Collusion [61.557412796012535]
  Ensembles of machine learning models have been well established as a powerful method of improving performance over a single model. Traditionally, ensembling algorithms train their base learners independently or sequentially with the goal of optimizing their joint performance. We show that directly minimizing the loss of the ensemble appears to rarely be applied in practice.
  arXiv  Detail & Related papers  (2023-01-26T18:58:07Z)
• Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints [51.12047280149546]
  A direct approach for obtaining a fair predictive model is to train the model through optimizing its prediction performance subject to fairness constraints. We formulate the training problem of a fairness-aware machine learning model as an AUC optimization problem subject to a class of AUC-based fairness constraints. We demonstrate the effectiveness of our approach on real-world data under different fairness metrics.
  arXiv  Detail & Related papers  (2022-12-23T22:29:08Z)
• Domain Adaptation with Adversarial Training on Penultimate Activations [82.9977759320565]
  Enhancing model prediction confidence on unlabeled target data is an important objective in Unsupervised Domain Adaptation (UDA) We show that this strategy is more efficient and better correlated with the objective of boosting prediction confidence than adversarial training on input images or intermediate features.
  arXiv  Detail & Related papers  (2022-08-26T19:50:46Z)
• Balanced Self-Paced Learning for AUC Maximization [88.53174245457268]
  Existing self-paced methods are limited to pointwise AUC. Our algorithm converges to a stationary point on the basis of closed-form solutions.
  arXiv  Detail & Related papers  (2022-07-08T02:09:32Z)
• Improving Prediction of Low-Prior Clinical Events with Simultaneous General Patient-State Representation Learning [11.574235466142833]
  We study the approach in the context of Recurrent Neural Networks (RNNs) We show that the inclusion of general patient-state representation tasks during model training improves the prediction of individual low-prior targets.
  arXiv  Detail & Related papers  (2021-06-28T16:32:12Z)

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.