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Discussion of Breiman's "Two Cultures": From Two Cultures to One 论布莱曼的“两种文化”:从两种文化到一种文化
Pub Date : 2021-07-27 DOI: 10.1353/obs.2021.0004
Anna Neufeld, D. Witten
Abstract:We argue that algorithmic models, though powerful and appropriate in some circumstances, rely on just as many tenuous assumptions as parametric probabilistic models; these assumptions, their violations, and the ethical consequences of these violations are simply obscured within a black box. We advocate for a future in which statisticians play a central role in bridging the gap between Breiman's two cultures.
摘要:我们认为,尽管算法模型在某些情况下是强大和合适的,但它与参数概率模型一样依赖于许多脆弱的假设;这些假设,它们的违规行为,以及这些违规行为的道德后果,都被简单地隐藏在一个黑盒子里。我们提倡统计学家在弥合布雷曼两种文化之间的差距方面发挥核心作用的未来。
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引用次数: 1
A Melting Pot 熔炉
Pub Date : 2021-07-27 DOI: 10.1353/obs.2021.0012
R. Tibshirani, T. Hastie
Abstract:Leo Breiman's article "Statistical Modeling: The two cultures" was timely and provocative. He advocated for Statisticians to learn about and appreciate a different "culture": an algorithmic approach, as distinct from the familiar, stochastic, data modeling approach to Statistics. While we have appreciated and contributed to the algorithmic approach, we have always had a foot in both camps. Here we advocate for a "melting pot", arguing that both approaches have their virtues, sometimes on the same problem.
摘要:Leo Breiman的文章“统计建模:两种文化”是及时和挑衅性的。他主张统计学家了解并欣赏一种不同的“文化”:一种算法方法,与人们熟悉的随机统计数据建模方法不同。虽然我们对算法方法表示赞赏并做出了贡献,但我们始终涉足这两个阵营。在这里,我们提倡“大熔炉”,认为这两种方法都有各自的优点,有时是在同一个问题上。
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引用次数: 0
One Modern Culture of Statistics: Comments on Statistical Modeling: The Two Cultures (Breiman, 2001b) 一种现代统计学文化:对统计建模的评论:两种文化(Breiman,2001b)
Pub Date : 2021-07-27 DOI: 10.1353/obs.2021.0020
P. Bühlmann
Abstract:We comment on Leo Breiman's "Statistical Modeling: The Two Cultures" paper. We provide some thoughts on prediction from a broader perspective and argue that "aiming for one modern culture" is a highly embracing attempt for addressing key problems in data and information sciences.
摘要:我们评论了Leo Breiman的“统计建模:两种文化”论文。我们从更广泛的角度提供了一些关于预测的想法,并认为“以一种现代文化为目标”是解决数据和信息科学关键问题的一种高度包容的尝试。
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引用次数: 0
Causal Modelling: The Two Cultures 因果模型:两种文化
Pub Date : 2021-07-27 DOI: 10.1353/obs.2021.0006
Elizabeth L. Ogburn, I. Shpitser
Abstract:We offer descriptive and normative standards for the principled pursuit of causal inference. These standards address critiques of both the algorithmic and the data modeling cultures identified in (Breiman, 2001), and provide a fruitful synthesis of both cultures. We contrast the resulting "cautious causal inference" with overly optimistic methods inspired by algorithmic data analysis methods prevalent in machine learning, as well as older approaches to causal modeling that employ overly restrictive parametric models.
摘要:我们为因果推理的原则性追求提供了描述性和规范性的标准。这些标准解决了(Breiman, 2001)中对算法和数据建模文化的批评,并提供了两种文化的富有成效的综合。我们将由此产生的“谨慎因果推理”与机器学习中流行的算法数据分析方法所启发的过于乐观的方法,以及采用过度限制参数模型的旧因果建模方法进行了对比。
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引用次数: 2
Statistical Modeling: Returning to its Roots 统计建模:回归本源
Pub Date : 2021-07-27 DOI: 10.1353/obs.2021.0014
Qingyuan Zhao
Abstract:Leo Breiman's "Statistical Modeling: The Two Cultures" is a treasure for any statistician who engages with real world problem. I argue that there is a more fundamental dichotomy between the principles of statistical modeling and the techniques for statistical modeling. Focusing entirely on the techniques in statistical education and research can be dangerous. I join Breiman's call for statistics to return to its roots.
摘要:Leo Breiman的《统计建模:两种文化》是任何一位从事现实世界问题的统计学家的财富。我认为,统计建模的原理和统计建模的技术之间存在着更根本的二分法。完全专注于统计教育和研究中的技术可能是危险的。我和布莱曼一样呼吁统计数据回归其根源。
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引用次数: 1
Beyond Two Cultures: Cultural Infrastructure for Data-driven Decision Support. 超越两种文化:数据驱动决策支持的文化基础设施。
Pub Date : 2021-07-01 DOI: 10.1353/obs.2021.0024
Nikki L B Freeman, John Sperger, Helal El-Zaatari, Anna R Kahkoska, Minxin Lu, Michael Valancius, Arti V Virkud, Tarek M Zikry, Michael R Kosorok

In the twenty years since Dr. Leo Breiman's incendiary paper Statistical Modeling: The Two Cultures was first published, algorithmic modeling techniques have gone from controversial to commonplace in the statistical community. While the widespread adoption of these methods as part of the contemporary statistician's toolkit is a testament to Dr. Breiman's vision, the number of high-profile failures of algorithmic models suggests that Dr. Breiman's final remark that "the emphasis needs to be on the problem and the data" has been less widely heeded. In the spirit of Dr. Breiman, we detail an emerging research community in statistics - data-driven decision support. We assert that to realize the full potential of decision support, broadly and in the context of precision health, will require a culture of social awareness and accountability, in addition to ongoing attention towards complex technical challenges.

自Leo Breiman博士的煽动性论文《统计建模:两种文化》首次发表以来的20年里,算法建模技术在统计界已经从有争议的变成了司空见惯的事情。这些方法作为当代统计学家工具箱的一部分被广泛采用,这证明了布雷曼博士的远见,但算法模型的大量引人注目的失败表明,布雷曼博士的最后一句话“重点需要放在问题和数据上”并没有得到广泛的重视。本着Breiman博士的精神,我们详细介绍了一个新兴的统计学研究社区——数据驱动的决策支持。我们认为,要在广泛和精确保健的背景下充分发挥决策支持的潜力,除了持续关注复杂的技术挑战外,还需要一种社会意识和问责制的文化。
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引用次数: 0
Considerations Across Three Cultures: Parametric Regressions, Interpretable Algorithms, and Complex Algorithms. 跨越三种文化的考量:参数回归、可解释算法和复杂算法。
Pub Date : 2021-07-01 DOI: 10.1353/obs.2021.0009
Ani Eloyan, Sherri Rose

We consider an extension of Leo Breiman's thesis from "Statistical Modeling: The Two Cultures" to include a bifurcation of algorithmic modeling, focusing on parametric regressions, interpretable algorithms, and complex (possibly explainable) algorithms.

我们考虑扩展 Leo Breiman 在《统计建模:两种文化》一文中的论点:两种文化 "的延伸,将算法建模的分叉纳入其中,重点关注参数回归、可解释算法和复杂(可能可解释)算法。
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引用次数: 0
Causation in Action: Some Remarks Attendant to Re-reading Hill (1965) 作用中的因果关系:重读希尔(1965)的一些评论
Pub Date : 2021-06-04 DOI: 10.1353/OBS.2020.0007
Herbert L. Smith
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引用次数: 0
Heterogeneous Subgroup Identification with Observational Data: A Case Study Based on the National Study of Learning Mindsets 基于观察数据的异质亚群识别:基于国家学习心态研究的个案研究
Pub Date : 2021-06-04 DOI: 10.1353/obs.2019.0010
Bryan Keller, Jianshen Chen, Tianyang Zhang
Abstract:In this paper, we use a two-step approach for heterogeneous subgroup identification with a synthetic data set motivated by the National Study of Learning Mindsets. In the first step, optimal full propensity score matching is used to estimate stratum-specific treatment effects. In the second step, regression trees identify key subgroups based on covariates for which the treatment effect varies. In working with regression trees, we emphasize the role of the cost-complexity tuning parameter, selected through permutation-based Type I error rate studies, in justifying inferential decision-making, which we contrast with graphical and quantitative exploration for future study. Results indicate that the mindset intervention was effective, overall, in improving student achievement. While our exploratory analyses identified XC, C1, and X1 as potential effect modifiers worthy of further study, we find no statistically significant evidence of effect heterogeneity with the exception of urbanicity category XC = 3, but the finding is not robust to propensity score estimation method.
摘要:本文采用一种两步法,利用国家学习心态研究(National Study of Learning mindset)的综合数据集进行异质性亚群识别。第一步,利用最优全倾向评分匹配来估计层特异性处理效果。在第二步中,回归树根据治疗效果变化的协变量确定关键子组。在使用回归树时,我们强调通过基于排列的I型错误率研究选择的成本-复杂性调整参数在证明推理决策中的作用,并将其与未来研究的图形和定量探索进行对比。结果表明,心态干预在提高学生成绩方面是有效的。虽然我们的探索性分析发现XC、C1和X1是值得进一步研究的潜在影响修饰因子,但除了城市化类别XC = 3外,我们没有发现统计学上显著的效应异质性证据,但这一发现对于倾向得分估计方法并不稳健。
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引用次数: 9
Comment on Cochran’s “Observational Studies” 评科克伦的“观察性研究”
Pub Date : 2021-06-04 DOI: 10.1353/obs.2015.0017
B. Hansen, Adam C. Sales
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引用次数: 0
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Observational studies
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