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The paper addresses a practical gap in causal estimation: generic pretrained embeddings of rich post-treatment data (text, images, video) are not optimized for the nuisance tasks a semiparametric estimator needs. It proposes learning a shared adapter on top of frozen embeddings for outcome prediction, treatment propensity, and surrogate marginalization, then using cross-fitted orthogonal scores for inference. The A4 Alzheimer's trial validation is a useful real-world test, though readers should weigh the efficiency claims against the stated nuisance-rate conditions.
SIRNET: Causal Adaptation of Pretrained Embeddings for Semiparametric Treatment-Effect Estimation Under Surrogacy
arXiv:2610.14011v1 Announce Type: new Abstract: Modern experiments increasingly produce rich post-treatment objects---such as text, images, and video---that may provide early surrogate signals for outcomes that are delayed, expensive, or observed only for a subset of participants. A pretrained encoder can turn such an object into a strong generic embedding, but the embedding is not trained for the estimation tasks required by a causal estimator and may emphasize information irrelevant to the causal target. We propose SIRNET, a Surrogate-Informed Representation adaptation framework for neural-NETwork-based causal estimation in the presence of unstructured post-treatment surrogates. Starting from pretrained surrogate embeddings, SIRNET learns a shared adapter jointly for outcome prediction, treatment propensity, and arm-specific surrogate marginalization, beyond the generic pretraining objective. The resulting covariate and surrogate representations provide a common interface for these nuisance tasks. The final treatment-effect estimate and confidence interval are obtained from cross-fitted orthogonal scores that attain the semiparametric efficiency bound under the stated nuisance-rate conditions. We also validate SIRNET's performance in synthetic and semisynthetic experiments, as well as the Anti-Amyloid in Asymptomatic Alzheimer's Disease (A4) trial, using frozen pretrained brain-MRI embeddings as surrogates for a long-term outcome.
Abstract:Modern experiments increasingly produce rich post-treatment objects---such as text, images, and video---that may provide early surrogate signals for outcomes that are delayed, expensive, or observed only for a subset of participants. A pretrained encoder can turn such an object into a strong generic embedding, but the embedding is not trained for the estimation tasks required by a causal estimator and may emphasize information irrelevant to the causal target. We propose SIRNET, a Surrogate-Informed Representation adaptation framework for neural-NETwork-based causal estimation in the presence of unstructured post-treatment surrogates. Starting from pretrained surrogate embeddings, SIRNET learns a shared adapter jointly for outcome prediction, treatment propensity, and arm-specific surrogate marginalization, beyond the generic pretraining objective. The resulting covariate and surrogate representations provide a common interface for these nuisance tasks. The final treatment-effect estimate and confidence interval are obtained from cross-fitted orthogonal scores that attain the semiparametric efficiency bound under the stated nuisance-rate conditions. We also validate SIRNET's performance in synthetic and semisynthetic experiments, as well as the Anti-Amyloid in Asymptomatic Alzheimer's Disease (A4) trial, using frozen pretrained brain-MRI embeddings as surrogates for a long-term outcome.
Machine Learning (cs.LG); Methodology (stat.ME)
arXiv:2610.14011 [cs.LG]
(or arXiv:2610.14011v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.14011 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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