"Data Monkeys": A Procedural Model of Extrapolation from Partial Statistics*
"Data Monkeys": A Procedural Model of Extrapolation from Partial Statistics*
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“数据猴子”：从部分统计数据推断的程序模型*
DOI：
10.1093/restud/rdx004
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Spiegler R
中科院分区：
文献类型：
--
作者：
Spiegler R
我提出了一个“数据分析师”的行为模型，他从一组涵盖部分重叠变量的统计数据集中推断出关于可观察变量的完全指定的概率分布。分析师使用迭代外推程序，其个别回合类似于输入缺失数据的随机回归方法。程序输出的用户无法区分原始数据和输入数据，这是他们的实际信念。我描述了这种信念扭曲底层数据生成过程的相关性结构的方式--重点关注可以描述为将因果模型(由可观察变量上的有向无环图表示)强加于真实分布的情况。
I present a behavioural model of a “data analyst” who extrapolates a fully specified probability distribution over observable variables from a collection of statistical data sets that cover partially overlapping sets of variables. The analyst employs an iterative extrapolation procedure, whose individual rounds are akin to the stochastic regression method of imputing missing data. Users of the procedure’s output fail to distinguish between raw and imputed data, and it functions as their practical belief. I characterize the ways in which this belief distorts the correlation structure of the underlying data generating process—focusing on cases in which the distortion can be described as the imposition of a causal model (represented by a directed acyclic graph over observable variables) on the true distribution.