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
- 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) - Optimizing Latent Graph Representations of Surgical Scenes for Zero-Shot
Domain Transfer [6.880129372917993]
We evaluate four object-centric approaches for domain generalization, establishing baseline performance.
We develop an optimized method specifically tailored for domain generalization, LG-DG, that includes a novel disentanglement loss function.
Our optimized approach, LG-DG, achieves an improvement of 9.28% over the best baseline approach.
arXiv Detail & Related papers (2024-03-11T17:36:11Z) - Adaptive Variance Thresholding: A Novel Approach to Improve Existing
Deep Transfer Vision Models and Advance Automatic Knee-Joint Osteoarthritis
Classification [0.11249583407496219]
Knee-Joint Osteoarthritis (KOA) is a prevalent cause of global disability and inherently complex to diagnose.
One promising classification avenue involves applying deep learning methods.
This study proposes a novel paradigm for improving post-training specialized classifiers.
arXiv Detail & Related papers (2023-11-10T00:17:07Z) - Stage-Aware Learning for Dynamic Treatments [4.033641609534417]
We propose a novel individualized learning method for dynamic treatment regimes.
We focus on prioritizing alignment between the observed treatment trajectory and the one obtained by the optimal regime across decision stages.
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 inverse probability weighted methods.
arXiv Detail & Related papers (2023-10-30T06:35:31Z) - Prediction-Oriented Bayesian Active Learning [51.426960808684655]
Expected predictive information gain (EPIG) is an acquisition function that measures information gain in the space of predictions rather than parameters.
EPIG leads to stronger predictive performance compared with BALD across a range of datasets and models.
arXiv Detail & Related papers (2023-04-17T10:59:57Z) - 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) - A Twin Neural Model for Uplift [59.38563723706796]
Uplift is a particular case of conditional treatment effect modeling.
We propose a new loss function defined by leveraging a connection with the Bayesian interpretation of the relative risk.
We show our proposed method is competitive with the state-of-the-art in simulation setting and on real data from large scale randomized experiments.
arXiv Detail & Related papers (2021-05-11T16:02:39Z)
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.