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.

翻译：通过重加权分布以最小化与目标分布的距离，是估计广泛因果效应的强大而灵活的策略，但在实践中可能具有挑战性，因为最优权重通常依赖于对底层数据生成过程的知识。本文聚焦于基于设计的权重，这类权重不纳入结果信息；突出的例子包括前瞻性队列研究、调查加权以及增强加权估计器中涉及加权的部分。在此类应用中，我们探讨了表示学习在实践中寻找理想权重的核心作用。与通常假设一个正确设定的表示方法不同，我们强调了由表示选择引起的误差，并概述了一个用于寻找能最小化此误差的合适表示的通用框架。基于近期结合平衡权重与神经网络的研究，我们提出了一种端到端的估计程序，该程序学习一个灵活的表示，同时保留了良好的理论性质。我们证明，该方法在一系列常见的因果推断任务中具有竞争力。