Regret Minimization for Causal Inference on Large Treatment Space

Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:946-954, 2021.

Abstract

Predicting which action (treatment) will lead to a better outcome is a central task in decision support systems. To build a prediction model in real situations, learning from observational data with a sampling bias is a critical issue due to the lack of randomized controlled trial (RCT) data. To handle such biased observational data, recent efforts in causal inference and counterfactual machine learning have focused on debiased estimation of the potential outcomes on a binary action space and the difference between them, namely, the individual treatment effect. When it comes to a large action space (e.g., selecting an appropriate combination of medicines for a patient), however, the regression accuracy of the potential outcomes is no longer sufficient in practical terms to achieve a good decision-making performance. This is because a high mean accuracy on the large action space does not guarantee the nonexistence of a single potential outcome misestimation that misleads the whole decision. Our proposed loss minimizes the classification error of whether or not the action is relatively good for the individual target among all feasible actions, which further improves the decision-making performance, as we demonstrate. We also propose a network architecture and a regularizer that extracts a debiased representation not only from the individual feature but also from the biased action for better generalization in large action spaces. Extensive experiments on synthetic and semi-synthetic datasets demonstrate the superiority of our method for large combinatorial action spaces.

Cite this Paper

BibTeX
@InProceedings{pmlr-v130-tanimoto21a, title = { Regret Minimization for Causal Inference on Large Treatment Space }, author = {Tanimoto, Akira and Sakai, Tomoya and Takenouchi, Takashi and Kashima, Hisashi}, booktitle = {Proceedings of The 24th International Conference on Artificial Intelligence and Statistics}, pages = {946--954}, year = {2021}, editor = {Banerjee, Arindam and Fukumizu, Kenji}, volume = {130}, series = {Proceedings of Machine Learning Research}, month = {13--15 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v130/tanimoto21a/tanimoto21a.pdf}, url = {https://proceedings.mlr.press/v130/tanimoto21a.html}, abstract = { Predicting which action (treatment) will lead to a better outcome is a central task in decision support systems. To build a prediction model in real situations, learning from observational data with a sampling bias is a critical issue due to the lack of randomized controlled trial (RCT) data. To handle such biased observational data, recent efforts in causal inference and counterfactual machine learning have focused on debiased estimation of the potential outcomes on a binary action space and the difference between them, namely, the individual treatment effect. When it comes to a large action space (e.g., selecting an appropriate combination of medicines for a patient), however, the regression accuracy of the potential outcomes is no longer sufficient in practical terms to achieve a good decision-making performance. This is because a high mean accuracy on the large action space does not guarantee the nonexistence of a single potential outcome misestimation that misleads the whole decision. Our proposed loss minimizes the classification error of whether or not the action is relatively good for the individual target among all feasible actions, which further improves the decision-making performance, as we demonstrate. We also propose a network architecture and a regularizer that extracts a debiased representation not only from the individual feature but also from the biased action for better generalization in large action spaces. Extensive experiments on synthetic and semi-synthetic datasets demonstrate the superiority of our method for large combinatorial action spaces. } }
Endnote
%0 Conference Paper %T Regret Minimization for Causal Inference on Large Treatment Space %A Akira Tanimoto %A Tomoya Sakai %A Takashi Takenouchi %A Hisashi Kashima %B Proceedings of The 24th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2021 %E Arindam Banerjee %E Kenji Fukumizu %F pmlr-v130-tanimoto21a %I PMLR %P 946--954 %U https://proceedings.mlr.press/v130/tanimoto21a.html %V 130 %X Predicting which action (treatment) will lead to a better outcome is a central task in decision support systems. To build a prediction model in real situations, learning from observational data with a sampling bias is a critical issue due to the lack of randomized controlled trial (RCT) data. To handle such biased observational data, recent efforts in causal inference and counterfactual machine learning have focused on debiased estimation of the potential outcomes on a binary action space and the difference between them, namely, the individual treatment effect. When it comes to a large action space (e.g., selecting an appropriate combination of medicines for a patient), however, the regression accuracy of the potential outcomes is no longer sufficient in practical terms to achieve a good decision-making performance. This is because a high mean accuracy on the large action space does not guarantee the nonexistence of a single potential outcome misestimation that misleads the whole decision. Our proposed loss minimizes the classification error of whether or not the action is relatively good for the individual target among all feasible actions, which further improves the decision-making performance, as we demonstrate. We also propose a network architecture and a regularizer that extracts a debiased representation not only from the individual feature but also from the biased action for better generalization in large action spaces. Extensive experiments on synthetic and semi-synthetic datasets demonstrate the superiority of our method for large combinatorial action spaces.
APA
Tanimoto, A., Sakai, T., Takenouchi, T. & Kashima, H.. (2021). Regret Minimization for Causal Inference on Large Treatment Space . Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 130:946-954 Available from https://proceedings.mlr.press/v130/tanimoto21a.html.

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