An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series

Qiang Huang, Chuizheng Meng, Defu Cao, Biwei Huang, Yi Chang, Yan Liu
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:20043-20062, 2024.

Abstract

Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the mitigation of treatment bias. The balancing strategy aimed at reducing covariate disparities between different treatment groups serves as a universal solution. However, when it comes to the time series data, the effectiveness of balancing strategies remains an open question, with a thorough analysis of the robustness and applicability of balancing strategies still lacking. This paper revisits counterfactual estimation in the temporal setting and provides a brief overview of recent advancements in balancing strategies. More importantly, we conduct a critical empirical examination for the effectiveness of the balancing strategies within the realm of temporal counterfactual estimation in various settings on multiple datasets. Our findings could be of significant interest to researchers and practitioners and call for a reexamination of the balancing strategy in time series settings.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-huang24r, title = {An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series}, author = {Huang, Qiang and Meng, Chuizheng and Cao, Defu and Huang, Biwei and Chang, Yi and Liu, Yan}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {20043--20062}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/huang24r/huang24r.pdf}, url = {https://proceedings.mlr.press/v235/huang24r.html}, abstract = {Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the mitigation of treatment bias. The balancing strategy aimed at reducing covariate disparities between different treatment groups serves as a universal solution. However, when it comes to the time series data, the effectiveness of balancing strategies remains an open question, with a thorough analysis of the robustness and applicability of balancing strategies still lacking. This paper revisits counterfactual estimation in the temporal setting and provides a brief overview of recent advancements in balancing strategies. More importantly, we conduct a critical empirical examination for the effectiveness of the balancing strategies within the realm of temporal counterfactual estimation in various settings on multiple datasets. Our findings could be of significant interest to researchers and practitioners and call for a reexamination of the balancing strategy in time series settings.} }
Endnote
%0 Conference Paper %T An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series %A Qiang Huang %A Chuizheng Meng %A Defu Cao %A Biwei Huang %A Yi Chang %A Yan Liu %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-huang24r %I PMLR %P 20043--20062 %U https://proceedings.mlr.press/v235/huang24r.html %V 235 %X Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the mitigation of treatment bias. The balancing strategy aimed at reducing covariate disparities between different treatment groups serves as a universal solution. However, when it comes to the time series data, the effectiveness of balancing strategies remains an open question, with a thorough analysis of the robustness and applicability of balancing strategies still lacking. This paper revisits counterfactual estimation in the temporal setting and provides a brief overview of recent advancements in balancing strategies. More importantly, we conduct a critical empirical examination for the effectiveness of the balancing strategies within the realm of temporal counterfactual estimation in various settings on multiple datasets. Our findings could be of significant interest to researchers and practitioners and call for a reexamination of the balancing strategy in time series settings.
APA
Huang, Q., Meng, C., Cao, D., Huang, B., Chang, Y. & Liu, Y.. (2024). An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:20043-20062 Available from https://proceedings.mlr.press/v235/huang24r.html.

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