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.

翻译：我们提出了ScoreMatchingRiesz，一个基于分数匹配的Riesz表示子估计器族。Riesz表示子是去偏机器学习中的一个关键干扰参数，它通过Neyman正交分数实现了因果与结构目标的$\sqrt{n}$一致性及渐近有效估计。我们将Riesz表示子估计问题表述为一个分数估计问题。这一视角通过允许我们利用去噪分数匹配和伸缩密度比估计，稳定了表示子的估计。我们还引入了政策路径参数，该参数捕捉了在连续处理下政策效应如何演变。我们证明了政策路径可以通过分数匹配进行估计，它平滑地连接了平均边际效应与平均政策效应的估计，从而提升了政策效应的可解释性。