Identification and Estimation of Conditional Average Partial Causal Effects via Instrumental Variable
This work addresses the need for more robust causal inference methods in fields like economics and social sciences, offering incremental improvements over prior approaches.
The paper tackles the problem of estimating heterogeneous causal effects with continuous treatment by introducing conditional average partial causal effects (CAPCE) and providing identification conditions under weaker assumptions than existing methods, developing sieve, parametric, and RKHS-based estimators and demonstrating their performance on synthetic and real-world data.
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.