Treatment Effect Learning Under Sequential Randomization
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911227918155776 |
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| author | Friedberg, Rina Mudd, Richard Johnstone, Patrick Pothen, Melissa Vaingankar, Vishal Sangale, Vishwanath Zaidi, Abbas |
| author_facet | Friedberg, Rina Mudd, Richard Johnstone, Patrick Pothen, Melissa Vaingankar, Vishal Sangale, Vishwanath Zaidi, Abbas |
| contents | Sequential treatment assignments in online experiments lead to complex dependency structures, often rendering identification, estimation and inference over treatments a challenge. Treatments in one session (e.g., a user logging on) can have an effect that persists into subsequent sessions, leading to cumulative effects on outcomes measured at a later stage. This can render standard methods for identification and inference trivially misspecified. We propose T-Learners layered into the G-Formula for this setting, building on literature from causal machine learning and identification in sequential settings. In a simple simulation, this approach prevents decaying accuracy in the presence of carry-over effects, highlighting the importance of identification and inference strategies tailored to the nature of systems often seen in the tech domain. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_20078 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Treatment Effect Learning Under Sequential Randomization Friedberg, Rina Mudd, Richard Johnstone, Patrick Pothen, Melissa Vaingankar, Vishal Sangale, Vishwanath Zaidi, Abbas Applications Methodology Sequential treatment assignments in online experiments lead to complex dependency structures, often rendering identification, estimation and inference over treatments a challenge. Treatments in one session (e.g., a user logging on) can have an effect that persists into subsequent sessions, leading to cumulative effects on outcomes measured at a later stage. This can render standard methods for identification and inference trivially misspecified. We propose T-Learners layered into the G-Formula for this setting, building on literature from causal machine learning and identification in sequential settings. In a simple simulation, this approach prevents decaying accuracy in the presence of carry-over effects, highlighting the importance of identification and inference strategies tailored to the nature of systems often seen in the tech domain. |
| title | Treatment Effect Learning Under Sequential Randomization |
| topic | Applications Methodology |
| url | https://arxiv.org/abs/2510.20078 |