aiqbench analysis
This research advances causal inference by leveraging data symmetries as shape constraints within partial identification frameworks, offering a theoretically grounded method to tighten bounds on unidentifiable effects. By integrating invariance principles into optimization problems, the approach provides more informative estimates for policy decisions without relying solely on traditional domain knowledge assumptions. The work represents a significant theoretical contribution to machine learning that enhances robustness in scenarios where standard causal effects cannot be precisely identified.
Symmetry-Informed Causal Partial Identification
arXiv:2610.09230v1 Announce Type: new Abstract: Partial identification (PI) entails estimating bounds on causal effects by encoding different assumptions on data generation as a constrained optimization problem. Such bounds can suffice to inform policy decisions even if the causal effect itself is not identifiable. Often vacuous in practice, practitioners seek to exhaustively encode domain knowledge as additional constraints to make the PI bounds more informative. We introduce known data symmetries -- invariance of the causal effect under certain data transformations -- as a new source of constraints to inform PI. We operationalize this as a shape constraint on the causal function, and via a change of measure against which PI is posed using simple data pre-processing. Both approaches are shown to sharpen bounds under two canonical PI models. This is shown both theoretically for the population case, and via experiments in the finite-sample case. More broadly, our framework establishes data symmetries as a natural, underutilized source of background knowledge for robust causal inference.
Abstract:Partial identification (PI) entails estimating bounds on causal effects by encoding different assumptions on data generation as a constrained optimization problem. Such bounds can suffice to inform policy decisions even if the causal effect itself is not identifiable. Often vacuous in practice, practitioners seek to exhaustively encode domain knowledge as additional constraints to make the PI bounds more informative. We introduce known data symmetries -- invariance of the causal effect under certain data transformations -- as a new source of constraints to inform PI. We operationalize this as a shape constraint on the causal function, and via a change of measure against which PI is posed using simple data pre-processing. Both approaches are shown to sharpen bounds under two canonical PI models. This is shown both theoretically for the population case, and via experiments in the finite-sample case. More broadly, our framework establishes data symmetries as a natural, underutilized source of background knowledge for robust causal inference.
Machine Learning (cs.LG)
arXiv:2610.09230 [cs.LG]
(or arXiv:2610.09230v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.09230 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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