There has been considerable recent interest in estimating heterogeneous causal effects. In this paper, we introduce conditional average partial causal effects (CAPCE) to reveal the heterogeneity of causal effects with continuous treatment. We provide conditions for identifying CAPCE in an instrumental variable setting. We develop three families of CAPCE estimators: sieve, parametric, and reproducing kernel Hilbert space (RKHS)-based, and analyze their statistical properties. We illustrate the proposed CAPCE estimators on synthetic and real-world data.

翻译：近年来，异质性因果效应的估计引起了广泛关注。本文引入条件平均部分因果效应（CAPCE），以揭示连续处理变量下因果效应的异质性。我们给出了在工具变量设定下识别CAPCE的条件，并发展了三种CAPCE估计方法：级数法、参数法以及基于再生核希尔伯特空间（RKHS）的方法，同时分析了它们的统计性质。最后，通过合成数据和真实世界数据对所提出的CAPCE估计量进行了验证。