A New Representation Learning Method for Individual Treatment Effect Estimation: Split Covariate Representation Network

Liu Qidong, Tian Feng, Ji Weihua, Zheng Qinghua
Proceedings of The 12th Asian Conference on Machine Learning, PMLR 129:811-822, 2020.

Abstract

Individual treatment effect (ITE) estimation is widely used in many essential fields, such as medical and education. But two problems, unknown counterfactual outcome and confounder, are the barriers for making a good ITE estimation. Although some representation learning methods based on potential outcome framework have been proposed to solve the problems, we find that most of previous works assume all features (also named covariate) of a unit are confounders. However, this assumption is not easy to become true, because instrument variables, adjustment variables and irrelevant variables can also be included in features. Therefore, this paper proposes a simple method to split covariates, and then a network, Split Covariate Representation Network (SCRNet), is mentioned, which is used to estimate ITE by different kinds of variables. Experiment results show that our method outperforms other state-of-arts methods on IHDP, a semi-synthetic dataset, and Jobs, a real-world dataset.

Cite this Paper

BibTeX
@InProceedings{pmlr-v129-qidong20a, title = {A New Representation Learning Method for Individual Treatment Effect Estimation: Split Covariate Representation Network}, author = {Qidong, Liu and Feng, Tian and Weihua, Ji and Qinghua, Zheng}, booktitle = {Proceedings of The 12th Asian Conference on Machine Learning}, pages = {811--822}, year = {2020}, editor = {Pan, Sinno Jialin and Sugiyama, Masashi}, volume = {129}, series = {Proceedings of Machine Learning Research}, month = {18--20 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v129/qidong20a/qidong20a.pdf}, url = {https://proceedings.mlr.press/v129/qidong20a.html}, abstract = {Individual treatment effect (ITE) estimation is widely used in many essential fields, such as medical and education. But two problems, unknown counterfactual outcome and confounder, are the barriers for making a good ITE estimation. Although some representation learning methods based on potential outcome framework have been proposed to solve the problems, we find that most of previous works assume all features (also named covariate) of a unit are confounders. However, this assumption is not easy to become true, because instrument variables, adjustment variables and irrelevant variables can also be included in features. Therefore, this paper proposes a simple method to split covariates, and then a network, Split Covariate Representation Network (SCRNet), is mentioned, which is used to estimate ITE by different kinds of variables. Experiment results show that our method outperforms other state-of-arts methods on IHDP, a semi-synthetic dataset, and Jobs, a real-world dataset.} }
Endnote
%0 Conference Paper %T A New Representation Learning Method for Individual Treatment Effect Estimation: Split Covariate Representation Network %A Liu Qidong %A Tian Feng %A Ji Weihua %A Zheng Qinghua %B Proceedings of The 12th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Sinno Jialin Pan %E Masashi Sugiyama %F pmlr-v129-qidong20a %I PMLR %P 811--822 %U https://proceedings.mlr.press/v129/qidong20a.html %V 129 %X Individual treatment effect (ITE) estimation is widely used in many essential fields, such as medical and education. But two problems, unknown counterfactual outcome and confounder, are the barriers for making a good ITE estimation. Although some representation learning methods based on potential outcome framework have been proposed to solve the problems, we find that most of previous works assume all features (also named covariate) of a unit are confounders. However, this assumption is not easy to become true, because instrument variables, adjustment variables and irrelevant variables can also be included in features. Therefore, this paper proposes a simple method to split covariates, and then a network, Split Covariate Representation Network (SCRNet), is mentioned, which is used to estimate ITE by different kinds of variables. Experiment results show that our method outperforms other state-of-arts methods on IHDP, a semi-synthetic dataset, and Jobs, a real-world dataset.
APA
Qidong, L., Feng, T., Weihua, J. & Qinghua, Z.. (2020). A New Representation Learning Method for Individual Treatment Effect Estimation: Split Covariate Representation Network. Proceedings of The 12th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 129:811-822 Available from https://proceedings.mlr.press/v129/qidong20a.html.

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