Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments
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arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909216871022592 |
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| author | Tran, Allen Bibaut, Aurélien Kallus, Nathan |
| author_facet | Tran, Allen Bibaut, Aurélien Kallus, Nathan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_08527 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments Tran, Allen Bibaut, Aurélien Kallus, Nathan Applications Methodology 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. |
| title | Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments |
| topic | Applications Methodology |
| url | https://arxiv.org/abs/2311.08527 |