Instrumental Variable Estimation of Average Partial Causal Effects
Abstract
Instrumental variable (IV) analysis is a powerful tool widely used to elucidate causal relationships. We study the problem of estimating the average partial causal effect (APCE) of a continuous treatment in an IV setting. Specifically, we develop new methods for estimating APCE based on a recent identification condition via an integral equation. We develop two families of methods, nonparametric and parametric - the former uses the Picard iteration to solve the integral equation; the latter parameterizes APCE using a linear basis function model. We analyze the statistical and computational properties of the proposed methods and illustrate them on synthetic and real data.
Cite
Text
Kawakami et al. "Instrumental Variable Estimation of Average Partial Causal Effects." International Conference on Machine Learning, 2023.Markdown
[Kawakami et al. "Instrumental Variable Estimation of Average Partial Causal Effects." International Conference on Machine Learning, 2023.](https://mlanthology.org/icml/2023/kawakami2023icml-instrumental/)BibTeX
@inproceedings{kawakami2023icml-instrumental,
title = {{Instrumental Variable Estimation of Average Partial Causal Effects}},
author = {Kawakami, Yuta and Kuroki, Manabu and Tian, Jin},
booktitle = {International Conference on Machine Learning},
year = {2023},
pages = {16097-16130},
volume = {202},
url = {https://mlanthology.org/icml/2023/kawakami2023icml-instrumental/}
}