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[2602.17070] General sample size analysis for probabilities of causation: a delta method approach
Summary
This paper presents a delta method approach for sample size analysis in estimating probabilities of causation (PoCs), addressing the need for reliable sample size determination in decision-making contexts.
Why It Matters
Understanding the sample size required for accurate estimation of probabilities of causation is crucial in various fields, including statistics and artificial intelligence. This research fills a gap in existing methodologies by providing a framework that enhances decision-making processes based on empirical data.
Key Takeaways
- Introduces a delta method framework for sample size analysis.
- Focuses on probabilities of causation, crucial for decision-making.
- Demonstrates through simulations the stability of the proposed sample size calculations.
Statistics > Methodology arXiv:2602.17070 (stat) [Submitted on 19 Feb 2026] Title:General sample size analysis for probabilities of causation: a delta method approach Authors:Tianyuan Cheng, Ruirui Mao, Judea Pearl, Ang Li View a PDF of the paper titled General sample size analysis for probabilities of causation: a delta method approach, by Tianyuan Cheng and 3 other authors View PDF HTML (experimental) Abstract:Probabilities of causation (PoCs), such as the probability of necessity and sufficiency (PNS), are important tools for decision making but are generally not point identifiable. Existing work has derived bounds for these quantities using combinations of experimental and observational data. However, there is very limited research on sample size analysis, namely, how many experimental and observational samples are required to achieve a desired margin of error. In this paper, we propose a general sample size framework based on the delta method. Our approach applies to settings in which the target bounds of PoCs can be expressed as finite minima or maxima of linear combinations of experimental and observational probabilities. Through simulation studies, we demonstrate that the proposed sample size calculations lead to stable estimation of these bounds. Subjects: Methodology (stat.ME); Artificial Intelligence (cs.AI) Cite as: arXiv:2602.17070 [stat.ME] (or arXiv:2602.17070v1 [stat.ME] for this version) https://doi.org/10.48550/arXiv.2602.17070 Focus to learn more arX...