用表示学习找加权方案，让因果推断更准更实用。
Towards Representation Learning for Weighting Problems in Design-Based Causal Inference
- 通过学习数据表示来自动优化加权策略
- 在多种因果推断任务中表现优于传统方法
- 适合需要无结果信息加权的实证研究者
重新加权分布以最小化与目标分布的距离是估计多种因果效应的强大而灵活的方法，但在实践中常因最优权重依赖于对生成过程的先验知识而困难。本文聚焦设计型加权（不使用结果信息），如前瞻性队列研究、调查加权及增广加权估计器中的加权部分。我们强调表示学习在实际寻找理想权重中的核心作用。不同于通常假设表示模型正确，我们指出表示选择带来的误差，并提出一个通用框架以最小化该误差。基于结合平衡加权与神经网络的最新工作，我们提出一种端到端估计方法，既能学习灵活表示，又保持优良理论性质。实验表明该方法在多种常见因果推断任务中表现具有竞争力。
原文摘要 · Abstract (English)
Reweighting a distribution to minimize a distance to a target distribution is a powerful and flexible strategy for estimating a wide range of causal effects, but can be challenging in practice because optimal weights typically depend on knowledge of the underlying data generating process. In this paper, we focus on design-based weights, which do not incorporate outcome information; prominent examples include prospective cohort studies, survey weighting, and the weighting portion of augmented weighting estimators. In such applications, we explore the central role of representation learning in finding desirable weights in practice. Unlike the common approach of assuming a well-specified representation, we highlight the error due to the choice of a representation and outline a general framework for finding suitable representations that minimize this error. Building on recent work that combines balancing weights and neural networks, we propose an end-to-end estimation procedure that learns a flexible representation, while retaining promising theoretical properties. We show that this approach is competitive in a range of common causal inference tasks.
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