About关于

Sources来源

The labs are short explanations of standard ideas — confounding, matching, community structure, contagion on a graph, overfitting, regularization, and reading text as counts or coordinates. The books below are where those ideas are taught.这些实验是对一些标准想法的短说明：混杂、匹配、社区结构、图上的传染、过拟合、正则化，以及把文本看成计数或坐标。下面的书是这些想法的出处。

Books书

Causal因果

• Miguel A. Hernán and James M. Robins, Causal Inference: What If. Topics: confounding, exchangeability, and why an association is not yet an effect. The lab is the linear special case in which one common cause is omitted.。主题：混杂、可交换性，以及为何关联还不是效应。实验是省略一个共同原因的线性特例。 Author page for the book作者的书页.
• Judea Pearl, Causality. Topic: causal diagrams. A confounder is drawn as a common cause. The lab uses one small diagram, not a general identification proof.。主题：因果图。混杂因素画成共同原因。实验只用一张小图，不是一般的识别证明。 Book site图书网站.
• Joshua D. Angrist and Jörn-Steffen Pischke, Mostly Harmless Econometrics (Princeton University Press). Topic: regression and omitted variables.（普林斯顿大学出版社）。主题：回归与遗漏变量。
• Scott Cunningham, Causal Inference: The Mixtape. Topic: confounding and regression adjustment, taught around the question of whether a slope is an effect.。主题：混杂与回归调整，围绕“斜率是不是效应”来讲。 Book site图书网站.
• Paul R. Rosenbaum and Donald B. Rubin (1983), “The central role of the propensity score in observational studies for causal effects,” Biometrika. Topic: a score that summarizes how covariates predict treatment. The matching lab has one covariate, so matching on X and matching on that score put units in the same order.。主题：用一个分数概括协变量如何预测处理。匹配实验只有一个协变量，所以按 X 匹配和按这个分数匹配，单位的顺序相同。
• Elizabeth A. Stuart (2010), “Matching methods for causal inference: a review and a look forward,” Statistical Science. Topics: calipers, covariate balance, and the fact that unmatched units leave the sample. The lab is a greedy one-to-one caliper match, not the review’s full catalogue of estimators.。主题：卡尺、协变量平衡，以及未匹配单位会离开样本。实验是贪心的一一卡尺匹配，不是那篇综述里的全部估计量。

Networks网络

• Stanley Wasserman and Katherine Faust, Social Network Analysis: Methods and Applications (Cambridge University Press). Topics: ties, paths, components, and subgroups. The lab reports components and a raw within-group edge share, not a full assortativity index.（剑桥大学出版社）。主题：连边、路径、连通分量和子群。实验报告连通分量和原始的组内边占比，不是完整的同配指数。
• Paul W. Holland, Kathryn Blackmond Laskey, and Samuel Leinhardt (1983) on stochastic block models, as cited in the NetworkX stochastic block model documentation. Topic: ties drawn independently from group-pair probabilities. The lab is given the groups. It does not estimate them.。主题：按组对概率独立抽取连边。实验被给定了组。它不估计组。
• Mark Newman, Networks: An Introduction (Oxford University Press). Topic: an epidemic on a graph. The diffusion lab is a susceptible–infected process on the same two-group graph. An outbreak cannot leave the index case’s component, and a bridge is what lets it reach the other group.（牛津大学出版社）。主题：图上的流行病。扩散实验是同一张两组图上的易感—感染过程。疫情不能离开指示病例所在的连通分量，桥让它到达另一组。

Machine learning机器学习

• Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani, An Introduction to Statistical Learning. Topics: the bias–variance tradeoff, overfitting, and the comparison of training and test error, including polynomial fits. The regularization lab uses the same book’s ridge and lasso idea: a penalty that shrinks slopes, and, for the absolute penalty, can set some slopes to zero.。主题：偏差—方差权衡、过拟合，以及训练误差与测试误差的比较，包括多项式拟合。正则化实验用同一本书里的岭回归和套索想法：惩罚缩小斜率；绝对惩罚还可以把一些斜率设成零。 Book site图书网站. Both labs use this site’s own sine-wave simulation.。两个实验都用本站自己的正弦波模拟。
• Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning. Topic, at a high level: more flexible functions fit a training sample more closely and can vary more across samples. The lab does not estimate bias and variance as separate terms.。主题，在较高层面上：更灵活的函数更贴近训练样本，也可能在不同样本之间变化更大。实验不把偏差和方差估计成分开的项。 Book site图书网站.

Text as data文本作为数据

• Justin Grimmer, Margaret E. Roberts, and Brandon M. Stewart, Text as Data文本作为数据 (Princeton University Press). Topics: choosing a representation, the bag of words, the document-term matrix, topic models as mixtures over K word distributions, and low-dimensional word representations. The labs use sentences written here. The topic-count page clusters counts and says so. The word-map page places coordinates by hand. Neither fits a topic model or trains an embedding, and neither reproduces a corpus analysis from the book.（普林斯顿大学出版社）。主题：选择表示、词袋、文档—词矩阵、作为 K 个词分布之混合的主题模型，以及词的低维表示。实验用这里写的句子。主题个数页对计数做聚类，并如此说明。词图页用手放置坐标。两者都不拟合主题模型，也不训练嵌入，也不复现书中的语料分析。

Interactive references交互式参考

These works informed the decision to teach with adjustable pictures. No third-party visualisation code or research data is bundled.这些工作影响了“用可调图形来教学”的决定。这里不捆绑第三方可视化代码或研究数据。

• Seeing Theory — reference for learning through adjustable visualisations.——通过可调图形学习的参考。
• Bynum and colleagues: An Interactive Introduction to Causal Inference — VISxAI 2022; source repository源代码仓库.
• NetworkX stochastic block model documentation — model definition and the Holland, Laskey & Leinhardt (1983) reference.——模型定义，以及 Holland、Laskey 与 Leinhardt（1983）的文献。
• scikit-learn: Underfitting vs. Overfitting — reference for the polynomial-regression teaching example; this site uses its own sine-wave simulation and validation sample.——多项式回归教学例子的参考；本站用自己的正弦波模拟和验证样本。
• Math Insight applets and MIT Mathlets — references for parameter-driven mathematical exploration.——参数驱动的数学探索的参考。
• Complexity Explorables — reference for interactive complex-systems models.——交互式复杂系统模型的参考。