Estimating Joint Treatment Effects by Combining Multiple Experiments

Yonghan Jung, Jin Tian, Elias Bareinboim
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:15451-15527, 2023.

Abstract

Estimating the effects of multi-dimensional treatments (i.e., joint treatment effects) is critical in many data-intensive domains, including genetics and drug evaluation. The main challenges for studying the joint treatment effects include the need for large sample sizes to explore different treatment combinations as well as potentially unsafe treatment interactions. In this paper, we develop machinery for estimating joint treatment effects by combining data from multiple experimental datasets. In particular, first, we develop new identification conditions for determining whether a joint treatment effect can be computed in terms of multiple interventional distributions under various scenarios. Further, we develop estimators with statistically appealing properties, including consistency and robustness to model misspecification and slow convergence. Finally, we perform simulation studies, which corroborate the effectiveness of the proposed methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-v202-jung23c, title = {Estimating Joint Treatment Effects by Combining Multiple Experiments}, author = {Jung, Yonghan and Tian, Jin and Bareinboim, Elias}, booktitle = {Proceedings of the 40th International Conference on Machine Learning}, pages = {15451--15527}, year = {2023}, editor = {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan}, volume = {202}, series = {Proceedings of Machine Learning Research}, month = {23--29 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v202/jung23c/jung23c.pdf}, url = {https://proceedings.mlr.press/v202/jung23c.html}, abstract = {Estimating the effects of multi-dimensional treatments (i.e., joint treatment effects) is critical in many data-intensive domains, including genetics and drug evaluation. The main challenges for studying the joint treatment effects include the need for large sample sizes to explore different treatment combinations as well as potentially unsafe treatment interactions. In this paper, we develop machinery for estimating joint treatment effects by combining data from multiple experimental datasets. In particular, first, we develop new identification conditions for determining whether a joint treatment effect can be computed in terms of multiple interventional distributions under various scenarios. Further, we develop estimators with statistically appealing properties, including consistency and robustness to model misspecification and slow convergence. Finally, we perform simulation studies, which corroborate the effectiveness of the proposed methods.} }
Endnote
%0 Conference Paper %T Estimating Joint Treatment Effects by Combining Multiple Experiments %A Yonghan Jung %A Jin Tian %A Elias Bareinboim %B Proceedings of the 40th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Andreas Krause %E Emma Brunskill %E Kyunghyun Cho %E Barbara Engelhardt %E Sivan Sabato %E Jonathan Scarlett %F pmlr-v202-jung23c %I PMLR %P 15451--15527 %U https://proceedings.mlr.press/v202/jung23c.html %V 202 %X Estimating the effects of multi-dimensional treatments (i.e., joint treatment effects) is critical in many data-intensive domains, including genetics and drug evaluation. The main challenges for studying the joint treatment effects include the need for large sample sizes to explore different treatment combinations as well as potentially unsafe treatment interactions. In this paper, we develop machinery for estimating joint treatment effects by combining data from multiple experimental datasets. In particular, first, we develop new identification conditions for determining whether a joint treatment effect can be computed in terms of multiple interventional distributions under various scenarios. Further, we develop estimators with statistically appealing properties, including consistency and robustness to model misspecification and slow convergence. Finally, we perform simulation studies, which corroborate the effectiveness of the proposed methods.
APA
Jung, Y., Tian, J. & Bareinboim, E.. (2023). Estimating Joint Treatment Effects by Combining Multiple Experiments. Proceedings of the 40th International Conference on Machine Learning, in Proceedings of Machine Learning Research 202:15451-15527 Available from https://proceedings.mlr.press/v202/jung23c.html.

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