Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments

Allen Tran, Aurelien Bibaut, Nathan Kallus
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:48565-48577, 2024.

Abstract

We study inference on the long-term causal effect of a continual exposure to a novel intervention, which we term a long-term treatment, based on an experiment involving only short-term observations. Key examples include the long-term health effects of regularly-taken medicine or of environmental hazards and the long-term effects on users of changes to an online platform. This stands in contrast to short-term treatments or "shocks," whose long-term effect can reasonably be mediated by short-term observations, enabling the use of surrogate methods. Long-term treatments by definition have direct effects on long-term outcomes via continual exposure, so surrogacy conditions cannot reasonably hold. We connect the problem with offline reinforcement learning, leveraging doubly-robust estimators to estimate long-term causal effects for long-term treatments and construct confidence intervals.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-tran24b, title = {Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments}, author = {Tran, Allen and Bibaut, Aurelien and Kallus, Nathan}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {48565--48577}, 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/tran24b/tran24b.pdf}, url = {https://proceedings.mlr.press/v235/tran24b.html}, abstract = {We study inference on the long-term causal effect of a continual exposure to a novel intervention, which we term a long-term treatment, based on an experiment involving only short-term observations. Key examples include the long-term health effects of regularly-taken medicine or of environmental hazards and the long-term effects on users of changes to an online platform. This stands in contrast to short-term treatments or "shocks," whose long-term effect can reasonably be mediated by short-term observations, enabling the use of surrogate methods. Long-term treatments by definition have direct effects on long-term outcomes via continual exposure, so surrogacy conditions cannot reasonably hold. We connect the problem with offline reinforcement learning, leveraging doubly-robust estimators to estimate long-term causal effects for long-term treatments and construct confidence intervals.} }
Endnote
%0 Conference Paper %T Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments %A Allen Tran %A Aurelien Bibaut %A Nathan Kallus %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-tran24b %I PMLR %P 48565--48577 %U https://proceedings.mlr.press/v235/tran24b.html %V 235 %X We study inference on the long-term causal effect of a continual exposure to a novel intervention, which we term a long-term treatment, based on an experiment involving only short-term observations. Key examples include the long-term health effects of regularly-taken medicine or of environmental hazards and the long-term effects on users of changes to an online platform. This stands in contrast to short-term treatments or "shocks," whose long-term effect can reasonably be mediated by short-term observations, enabling the use of surrogate methods. Long-term treatments by definition have direct effects on long-term outcomes via continual exposure, so surrogacy conditions cannot reasonably hold. We connect the problem with offline reinforcement learning, leveraging doubly-robust estimators to estimate long-term causal effects for long-term treatments and construct confidence intervals.
APA
Tran, A., Bibaut, A. & Kallus, N.. (2024). Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:48565-48577 Available from https://proceedings.mlr.press/v235/tran24b.html.

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