Identification and Estimation of Conditional Average Partial Causal Effects via Instrumental Variable

Yuta Kawakami, Manabu Kuroki, Jin Tian
Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence, PMLR 244:1922-1952, 2024.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v244-kawakami24b, title = {Identification and Estimation of Conditional Average Partial Causal Effects via Instrumental Variable}, author = {Kawakami, Yuta and Kuroki, Manabu and Tian, Jin}, booktitle = {Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence}, pages = {1922--1952}, year = {2024}, editor = {Kiyavash, Negar and Mooij, Joris M.}, volume = {244}, series = {Proceedings of Machine Learning Research}, month = {15--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v244/main/assets/kawakami24b/kawakami24b.pdf}, url = {https://proceedings.mlr.press/v244/kawakami24b.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Identification and Estimation of Conditional Average Partial Causal Effects via Instrumental Variable %A Yuta Kawakami %A Manabu Kuroki %A Jin Tian %B Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2024 %E Negar Kiyavash %E Joris M. Mooij %F pmlr-v244-kawakami24b %I PMLR %P 1922--1952 %U https://proceedings.mlr.press/v244/kawakami24b.html %V 244 %X 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.
APA
Kawakami, Y., Kuroki, M. & Tian, J.. (2024). Identification and Estimation of Conditional Average Partial Causal Effects via Instrumental Variable. Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 244:1922-1952 Available from https://proceedings.mlr.press/v244/kawakami24b.html.

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