Identification and Estimation of Conditional Average Partial Causal
Effects via Instrumental Variable
Conference on Uncertainty in Artificial Intelligence (UAI), 2024
- CML
Main:9 Pages
6 Figures
Bibliography:3 Pages
16 Tables
Appendix:19 Pages
Abstract
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.
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