Papers › Tailored inference for finite populations: conditional validity and transfer across...
Tailored inference for finite populations: conditional validity and transfer across distributions
Ying Jin, Dominik Rothenhäusler
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
Parameters of sub-populations can be more relevant than super-population ones. For example, a healthcare provider may be interested in the effect of a treatment plan for a specific subset of their patients; policymakers may be concerned with the impact of a policy in a particular state within a given population. In these cases, the focus is on a specific finite population, as opposed to an infinite super-population. Such a population can be characterized by fixing some attributes that are intrinsic to them, leaving unexplained variations like measurement error as random. Inference for a population with fixed attributes can then be modeled as inferring parameters of a conditional distribution. Accordingly, it is desirable that confidence intervals are conditionally valid for the realized population, instead of marginalizing over many possible draws of populations. We provide a statistical inference framework for parameters of finite populations with known attributes. Leveraging the attribute information, our estimators and confidence intervals closely target a specific finite population. When the data is from the population of interest, our confidence intervals attain asymptotic conditional validity given the attributes, and are shorter than those for super-population inference. In addition, we develop procedures to infer parameters of new populations with differing covariate distributions; the confidence intervals are also conditionally valid for the new populations under mild conditions. Our methods extend to situations where the fixed information has a weaker structure or is only partially observed. We demonstrate the validity and applicability of our methods using simulated and real-world data.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections