Power Analysis for Causal Discovery.

IF 3.4 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Data Science and Analytics Pub Date : 2024-04-01 Epub Date: 2023-06-27 DOI:10.1007/s41060-023-00399-4

Erich Kummerfeld, Leland Williams, Sisi Ma

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Abstract

Causal discovery algorithms have the potential to impact many fields of science. However, substantial foundational work on the statistical properties of causal discovery algorithms is still needed. This paper presents what is to our knowledge the first method for conducting power analysis for causal discovery algorithms. The power sample characteristics of causal discovery algorithms typically cannot be described by a closed formula, but we resolve this problem by developing a new power sample analysis method based on standardized in silico simulation experiments. Our procedure generates data with carefully controlled statistical effect sizes in order to enable an accurate numerical power sample analysis. We present that method, apply it to generate an initial power analysis table, provide a web interface for searching this table, and show how the table or web interface can be used to solve several types of real world power analysis problems, such as sample size planning, interpretation of results, and sensitivity analysis.

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因果发现的功率分析

因果发现算法有可能对许多科学领域产生影响。然而，在因果发现算法的统计特性方面仍需要大量的基础性工作。据我们所知，本文首次提出了对因果发现算法进行幂次分析的方法。因果发现算法的功率样本特征通常无法用封闭公式来描述，但我们通过开发一种基于标准化硅学模拟实验的新功率样本分析方法解决了这一问题。我们的程序生成的数据具有严格控制的统计效应大小，从而能够进行精确的数字功率样本分析。我们介绍了该方法，并将其应用于生成初始功率分析表，提供了一个用于搜索该表的网络界面，并展示了如何使用该表或网络界面来解决现实世界中的几类功率分析问题，如样本大小规划、结果解释和敏感性分析。

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来源期刊

International Journal of Data Science and Analytics Multiple-

CiteScore

6.40

自引率

8.30%

发文量

期刊介绍： Data Science has been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. The field encompasses the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. It also tackles related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation.The International Journal of Data Science and Analytics (JDSA) brings together thought leaders, researchers, industry practitioners, and potential users of data science and analytics, to develop the field, discuss new trends and opportunities, exchange ideas and practices, and promote transdisciplinary and cross-domain collaborations. The journal is composed of three streams: Regular, to communicate original and reproducible theoretical and experimental findings on data science and analytics; Applications, to report the significant data science applications to real-life situations; and Trends, to report expert opinion and comprehensive surveys and reviews of relevant areas and topics in data science and analytics.Topics of relevance include all aspects of the trends, scientific foundations, techniques, and applications of data science and analytics, with a primary focus on:statistical and mathematical foundations for data science and analytics;understanding and analytics of complex data, human, domain, network, organizational, social, behavior, and system characteristics, complexities and intelligences;creation and extraction, processing, representation and modelling, learning and discovery, fusion and integration, presentation and visualization of complex data, behavior, knowledge and intelligence;data analytics, pattern recognition, knowledge discovery, machine learning, deep analytics and deep learning, and intelligent processing of various data (including transaction, text, image, video, graph and network), behaviors and systems;active, real-time, personalized, actionable and automated analytics, learning, computation, optimization, presentation and recommendation; big data architecture, infrastructure, computing, matching, indexing, query processing, mapping, search, retrieval, interoperability, exchange, and recommendation;in-memory, distributed, parallel, scalable and high-performance computing, analytics and optimization for big data;review, surveys, trends, prospects and opportunities of data science research, innovation and applications;data science applications, intelligent devices and services in scientific, business, governmental, cultural, behavioral, social and economic, health and medical, human, natural and artificial (including online/Web, cloud, IoT, mobile and social media) domains; andethics, quality, privacy, safety and security, trust, and risk of data science and analytics