Partial Identification under Missing Data Using Weak Shadow Variables from Pretrained Models
arXiv:2602.16061v2 Announce Type: replace-cross Abstract: Estimating population quantities such as mean outcomes from user feedback is fundamental to platform evaluation and social science, yet feedback is often missing not at random (MNAR): users with stronger opinions are more likely to respond, so standard estimators are biased and the estimand is not identified without additional assumptions. Existing approaches typically rely on strong parametric assumptions or bespoke auxiliary variables that may be unavailable in practice. In this paper, we develop a partial identification framework in
The proliferation of pretrained models and the increasing need for reliable data in AI and social science research are driving innovations in handling missing data. This paper leverages these advances to address long-standing challenges in statistical inference.
This development improves the reliability and interpretability of data-driven insights derived from user feedback and other incomplete datasets, which are fundamental to AI system evaluation and evidence-based policy making. It addresses a critical source of bias in many real-world applications.
The ability to achieve partial identification with weak shadow variables from pretrained models means that more robust conclusions can be drawn from incomplete data without relying on strong, often unrealistic, assumptions. This offers a more flexible and accurate approach to statistical inference.
- · AI developers
- · Social scientists
- · Platform evaluators
- · Data science researchers
- · Organizations relying on simplistic missing data imputation and analyses
- · Researchers utilizing overly strong parametric assumptions without justification
Improved statistical rigor and reduced bias in studies reliant on feedback data, leading to more accurate insights into population quantities.
Increased trust in AI model evaluations and social science research outcomes that previously suffered from significant missing-not-at-random data issues.
Potentially faster iteration cycles for AI model development and policy interventions due to more dependable evaluation metrics and feedback analyses.
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Read at arXiv cs.LG