Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networks

• URL: http://arxiv.org/abs/2507.07738v1
• Date: Thu, 10 Jul 2025 13:16:33 GMT
• Title: Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networks
• Authors: Tomu Hirata, Undral Byambadalai, Tatsushi Oka, Shota Yasui, Shingo Uto,
• Abstract summary: We propose a novel multi-task neural network approach for estimating distributional treatment effects in randomized experiments.<n>We apply our method to both simulated and real-world datasets, including a randomized field experiment aimed at reducing water consumption in the US and a large-scale A/B test from a leading streaming platform in Japan.
• Score: 5.895315872876525
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a novel multi-task neural network approach for estimating distributional treatment effects (DTE) in randomized experiments. While DTE provides more granular insights into the experiment outcomes over conventional methods focusing on the Average Treatment Effect (ATE), estimating it with regression adjustment methods presents significant challenges. Specifically, precision in the distribution tails suffers due to data imbalance, and computational inefficiencies arise from the need to solve numerous regression problems, particularly in large-scale datasets commonly encountered in industry. To address these limitations, our method leverages multi-task neural networks to estimate conditional outcome distributions while incorporating monotonic shape constraints and multi-threshold label learning to enhance accuracy. To demonstrate the practical effectiveness of our proposed method, we apply our method to both simulated and real-world datasets, including a randomized field experiment aimed at reducing water consumption in the US and a large-scale A/B test from a leading streaming platform in Japan. The experimental results consistently demonstrate superior performance across various datasets, establishing our method as a robust and practical solution for modern causal inference applications requiring a detailed understanding of treatment effect heterogeneity.

Related papers

• A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation [55.53426007439564]
  Estimating individualized treatment effects from observational data is a central challenge in causal inference.<n>In inverse probability weighting (IPW) is a well-established solution to this problem, but its integration into modern deep learning frameworks remains limited.<n>We propose Importance-Weighted Diffusion Distillation (IWDD), a novel generative framework that combines the pretraining of diffusion models with importance-weighted score distillation.
  arXiv  Detail & Related papers  (2025-05-16T17:00:52Z)
• Reliable Deep Diffusion Tensor Estimation: Rethinking the Power of Data-Driven Optimization Routine [17.516054970588137]
  This work introduces a data-driven optimization-based method termed DoDTI. The proposed method attains state-of-the-art performance in DTI parameter estimation. Notably, it demonstrates superior generalization, accuracy, and efficiency, rendering it highly reliable for widespread application in the field.
  arXiv  Detail & Related papers  (2024-09-04T07:35:12Z)
• Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction [6.909352249236339]
  We propose a novel regression adjustment method designed for estimating distributional treatment effect parameters in randomized experiments. Our approach incorporates pre-treatment co-treatments into a distributional regression framework, utilizing machine learning techniques to improve the precision of distributional treatment effect estimators.
  arXiv  Detail & Related papers  (2024-07-22T20:28:29Z)
• Estimation of individual causal effects in network setup for multiple treatments [4.53340898566495]
  We study the problem of estimation of Individual Treatment Effects (ITE) in the context of multiple treatments and observational data. We employ Graph Convolutional Networks (GCN) to learn a shared representation of the confounders. Our approach utilizes separate neural networks to infer potential outcomes for each treatment.
  arXiv  Detail & Related papers  (2023-12-18T06:07:45Z)
• Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation [137.3520153445413]
  A notable gap exists in the evaluation of causal discovery methods, where insufficient emphasis is placed on downstream inference. We evaluate seven established baseline causal discovery methods including a newly proposed method based on GFlowNets. The results of our study demonstrate that some of the algorithms studied are able to effectively capture a wide range of useful and diverse ATE modes.
  arXiv  Detail & Related papers  (2023-07-11T02:58:10Z)
• Task-specific experimental design for treatment effect estimation [59.879567967089145]
  Large randomised trials (RCTs) are the standard for causal inference. Recent work has proposed more sample-efficient alternatives to RCTs, but these are not adaptable to the downstream application for which the causal effect is sought. We develop a task-specific approach to experimental design and derive sampling strategies customised to particular downstream applications.
  arXiv  Detail & Related papers  (2023-06-08T18:10:37Z)
• Learning Better with Less: Effective Augmentation for Sample-Efficient Visual Reinforcement Learning [57.83232242068982]
  Data augmentation (DA) is a crucial technique for enhancing the sample efficiency of visual reinforcement learning (RL) algorithms. It remains unclear which attributes of DA account for its effectiveness in achieving sample-efficient visual RL. This work conducts comprehensive experiments to assess the impact of DA's attributes on its efficacy.
  arXiv  Detail & Related papers  (2023-05-25T15:46:20Z)
• B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding [51.74479522965712]
  We propose a meta-learner called the B-Learner, which can efficiently learn sharp bounds on the CATE function under limits on hidden confounding. We prove its estimates are valid, sharp, efficient, and have a quasi-oracle property with respect to the constituent estimators under more general conditions than existing methods.
  arXiv  Detail & Related papers  (2023-04-20T18:07:19Z)
• DESCN: Deep Entire Space Cross Networks for Individual Treatment Effect Estimation [7.060064266376701]
  Causal Inference has wide applications in various areas such as E-commerce and precision medicine. This paper proposes Deep Entire Space Cross Networks (DESCN) to model treatment effects from an end-to-end perspective.
  arXiv  Detail & Related papers  (2022-07-19T01:25:31Z)
• Causal Inference from Small High-dimensional Datasets [7.1894784995284144]
  Causal-Batle is a methodology to estimate treatment effects in small high-dimensional datasets. We adopt an approach that brings transfer learning techniques into causal inference.
  arXiv  Detail & Related papers  (2022-05-19T02:04:01Z)
• SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data [83.50281440043241]
  We study the problem of inferring heterogeneous treatment effects from time-to-event data. We propose a novel deep learning method for treatment-specific hazard estimation based on balancing representations.
  arXiv  Detail & Related papers  (2021-10-26T20:13:17Z)

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.