提出新型得分匹配方法，用于无偏机器学习与政策路径估计
ScoreMatchingRiesz: Score Matching for Debiased Machine Learning and Policy Path Estimation
- 用得分匹配重构里茨表示器估计，提升稳定性
- 可实现因果效应的渐近高效估计，满足根号n收敛
- 首次将政策路径建模为连续处理下的效应演变轨迹
我们提出 ScoreMatchingRiesz，一种基于得分匹配的里茨表示器估计族。里茨表示器是无偏机器学习中的关键干扰成分，可通过奈曼正交得分实现因果与结构目标的根号n一致且渐近高效估计。我们将里茨表示器估计建模为得分估计问题，通过去噪得分匹配和级联密度比估计稳定了估计过程。我们还引入政策路径概念，用于捕捉连续处理下政策效应的变化过程。通过平滑连接平均边际效应（AME）与平均政策效应（APE）估计，可利用得分匹配实现政策路径估计，显著提升政策效应的可解释性。
原文摘要 · Abstract (English)
We propose ScoreMatchingRiesz, a family of Riesz representer estimators based on score matching. The Riesz representer is a key nuisance component in debiased machine learning, enabling $\sqrt{n}$-consistent and asymptotically efficient estimation of causal and structural targets via Neyman-orthogonal scores. We formulate Riesz representer estimation as a score estimation problem. This perspective stabilizes representer estimation by allowing us to leverage denoising score matching and telescoping density ratio estimation. We also introduce the policy path, a parameter that captures how policy effects evolve under continuous treatments. We show that the policy path can be estimated via score matching by smoothly connecting average marginal effect (AME) and average policy effect (APE) estimation, which improves the interpretability of policy effects.
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