There has been considerable recent interest in estimating heterogeneous causal effects. In this paper, we study 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. Notably, CAPCE is identifiable under a weaker assumption than required by a commonly used measure for estimating heterogeneous causal effects of continuous treatment. 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。本文研究条件平均部分因果效应，以揭示连续处理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。值得注意的是，相较于估计连续处理异质性因果效应的常用度量方法，CAPCE可在更弱的假设条件下被识别。我们开发了三类CAPCE估计量：基于筛法、参数化方法以及再生核希尔伯特空间的估计量，并分析了它们的统计性质。我们在合成数据与真实数据上对所提出的CAPCE估计量进行了验证。