Optimizing Treatment Allocation in the Presence of Interference

• URL: http://arxiv.org/abs/2410.00075v1
• Date: Mon, 30 Sep 2024 15:48:22 GMT
• Title: Optimizing Treatment Allocation in the Presence of Interference
• Authors: Daan Caljon, Jente Van Belle, Jeroen Berrevoets, Wouter Verbeke,
• Abstract summary: 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.
• Score: 6.404584255185188
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In Influence Maximization (IM), the objective is to -- given a budget -- select the optimal set of entities in a network to target with a treatment so as to maximize the total effect. For instance, in marketing, the objective is to target the set of customers that maximizes the total response rate, resulting from both direct treatment effects on targeted customers and indirect, spillover, effects that follow from targeting these customers. Recently, new methods to estimate treatment effects in the presence of network interference have been proposed. However, the issue of how to leverage these models to make better treatment allocation decisions has been largely overlooked. Traditionally, in Uplift Modeling (UM), entities are ranked according to estimated treatment effect, and the top entities are allocated treatment. Since, in a network context, entities influence each other, the UM ranking approach will be suboptimal. The problem of finding the optimal treatment allocation in a network setting is combinatorial and generally has to be solved heuristically. To fill the gap between IM and UM, we propose OTAPI: Optimizing Treatment Allocation in the Presence of Interference to find solutions to the IM problem using treatment effect estimates. OTAPI consists of two steps. First, a causal estimator is trained to predict treatment effects in a network setting. Second, this estimator is leveraged to identify an optimal treatment allocation by integrating it into classic IM algorithms. We demonstrate that this novel method outperforms classic IM and UM approaches on both synthetic and semi-synthetic datasets.

Related papers

• Dynamic Detection of Relevant Objectives and Adaptation to Preference Drifts in Interactive Evolutionary Multi-Objective Optimization [2.4374097382908477]
  We study the dynamic nature of DM preferences, which can evolve throughout the decision-making process and affect the relevance of objectives. We propose methods to discard outdated or conflicting preferences when such shifts occur. Our experimental results demonstrate that the proposed methods effectively manage evolving preferences and significantly enhance the quality and desirability of the solutions produced by the algorithm.
  arXiv  Detail & Related papers  (2024-11-07T09:09:06Z)
• Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System [75.25394449773052]
  Large Language Model (LLM) based multi-agent systems (MAS) show remarkable potential in collaborative problem-solving. Yet they still face critical challenges: low communication efficiency, poor scalability, and a lack of effective parameter-updating optimization methods. We present Optima, a novel framework that addresses these issues by significantly enhancing both communication efficiency and task effectiveness.
  arXiv  Detail & Related papers  (2024-10-10T17:00:06Z)
• Reduced-Rank Multi-objective Policy Learning and Optimization [57.978477569678844]
  In practice, causal researchers do not have a single outcome in mind a priori. In government-assisted social benefit programs, policymakers collect many outcomes to understand the multidimensional nature of poverty. We present a data-driven dimensionality-reduction methodology for multiple outcomes in the context of optimal policy learning.
  arXiv  Detail & Related papers  (2024-04-29T08:16:30Z)
• Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments [5.488412825534217]
  We introduce an active learning approach: Active Learning in Causal Inference with Interference (ACI) ACI uses Gaussian process to flexibly model the direct and spillover treatment effects as a function of a continuous measure of neighbors' treatment assignment. We demonstrate its feasibility in achieving accurate effects estimations with reduced data requirements.
  arXiv  Detail & Related papers  (2024-02-20T04:13:59Z)
• Taxonomy Adaptive Cross-Domain Adaptation in Medical Imaging via Optimization Trajectory Distillation [73.83178465971552]
  The success of automated medical image analysis depends on large-scale and expert-annotated training sets. Unsupervised domain adaptation (UDA) has been raised as a promising approach to alleviate the burden of labeled data collection. We propose optimization trajectory distillation, a unified approach to address the two technical challenges from a new perspective.
  arXiv  Detail & Related papers  (2023-07-27T08:58:05Z)
• Neighborhood Adaptive Estimators for Causal Inference under Network Interference [152.4519491244279]
  We consider the violation of the classical no-interference assumption, meaning that the treatment of one individuals might affect the outcomes of another. To make interference tractable, we consider a known network that describes how interference may travel. We study estimators for the average direct treatment effect on the treated in such a setting.
  arXiv  Detail & Related papers  (2022-12-07T14:53:47Z)
• Contingency-Aware Influence Maximization: A Reinforcement Learning Approach [52.109536198330126]
  influence (IM) problem aims at finding a subset of seed nodes in a social network that maximize the spread of influence. In this study, we focus on a sub-class of IM problems, where whether the nodes are willing to be the seeds when being invited is uncertain, called contingency-aware IM. Despite the initial success, a major practical obstacle in promoting the solutions to more communities is the tremendous runtime of the greedy algorithms.
  arXiv  Detail & Related papers  (2021-06-13T16:42:22Z)
• MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain Adaptation [84.90801699807426]
  This paper proposes an effective meta-optimization based strategy dubbed MetaAlign. We treat the domain alignment objective and the classification objective as the meta-train and meta-test tasks in a meta-learning scheme. Experimental results demonstrate the effectiveness of our proposed method on top of various alignment-based baseline approaches.
  arXiv  Detail & Related papers  (2021-03-25T03:16:05Z)
• Treatment Targeting by AUUC Maximization with Generalization Guarantees [7.837855832568568]
  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.
  arXiv  Detail & Related papers  (2020-12-17T19:32:35Z)
• Causal Bayesian Optimization [8.958125394444679]
  We study the problem of globally optimizing a variable of interest that is part of a causal model in which a sequence of interventions can be performed. Our approach combines ideas from causal inference, uncertainty quantification and sequential decision making. We show how knowing the causal graph significantly improves the ability to reason about optimal decision making strategies.
  arXiv  Detail & Related papers  (2020-05-24T13:20:50Z)
• Online Batch Decision-Making with High-Dimensional Covariates [20.06690325969748]
  We propose and investigate a class of new algorithms for sequential decision making that interact with textita batch of users simultaneously instead of textita user at each decision epoch. We deliver a solution, named textitTeamwork LASSO Bandit algorithm, that resolves a batch version of explore-exploit dilemma via switching between stage and selfish stage during the whole decision process.
  arXiv  Detail & Related papers  (2020-02-21T17:36:15Z)

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.