{"id":11244,"date":"2026-02-20T09:16:32","date_gmt":"2026-02-20T00:16:32","guid":{"rendered":"https:\/\/www.waseda.jp\/fpse\/winpec\/?p=11244"},"modified":"2026-02-20T09:16:32","modified_gmt":"2026-02-20T00:16:32","slug":"%e5%ae%9f%e8%a8%bc%e6%94%bf%e6%b2%bb%e5%ad%a6%e3%83%af%e3%83%bc%e3%82%af%e3%82%b7%e3%83%a7%e3%83%83%e3%83%97%e3%81%ae%e3%81%94%e6%a1%88%e5%86%85%ef%bc%88workshop-announcement-december-2-1700-3-2-3-2-2","status":"publish","type":"post","link":"https:\/\/www.waseda.jp\/fpse\/winpec\/news\/2026\/02\/20\/11244\/","title":{"rendered":"3\/5\uff08\u6728\uff09\u653f\u6cbb\u5b66\u65b9\u6cd5\u8ad6\u7814\u7a76\u90e8\u4f1a\u30fb\u65e9\u7a32\u7530\u56e0\u679c\u63a8\u8ad6\u30ef\u30fc\u30af\u30b7\u30e7\u30c3\u30d7\u5408\u540c\u958b\u50ac\u306e\u304a\u77e5\u3089\u305b"},"content":{"rendered":"

\u653f\u6cbb\u5b66\u65b9\u6cd5\u8ad6\u5206\u91ce\u3067\u6d3b\u8e8d\u3059\u308b\u7814\u7a76\u8005\u3092\u62db\u3044\u3066\u3001\u30ef\u30fc\u30af\u30b7\u30e7\u30c3\u30d7\u3092\u884c\u3044\u307e\u3059\u3002\u3069\u306a\u305f\u3067\u3082\u3054\u53c2\u52a0\u3044\u305f\u3060\u3051\u307e\u3059\u3002\u4eca\u56de\u306f\u3001\u65e9\u7a32\u7530\u56e0\u679c\u63a8\u8ad6\u30ef\u30fc\u30af\u30b7\u30e7\u30c3\u30d7(\u00a0https:\/\/causal.w.waseda.jp\/<\/a>\u00a0)<\/span>\u3068\u5408\u540c\u3067\u958b\u50ac\u3057\u307e\u3059\u3002\u4e8b\u524d\u767b\u9332\u306f\u4e0d\u8981\u3067\u3059\u3002\u596e\u3063\u3066\u3054\u53c2\u52a0\u304f\u3060\u3055\u3044\u3002<\/p>\n

\u767a\u8868\u8005<\/strong>\uff1a\u52dd\u53c8\u88d5\u6597\uff08\u6771\u4eac\u5927\u5b66\uff09<\/p>\n

\u65e5\u7a0b<\/strong>\uff1a3<\/span>\u67085<\/span>\u65e5\uff08\u6728\uff09\u00a013:30-15:00
\n<\/span>
\n\u5834\u6240<\/strong>\uff1a\u65e9\u7a32\u7530\u5927\u5b66\u65e9\u7a32\u7530\u30ad\u30e3\u30f3\u30d1\u30b93<\/span>\u53f7\u9928802<\/span>\u6559\u5ba4
\nhttps:\/\/www.waseda.jp\/inst\/student\/assets\/uploads\/2020\/08\/15_campusmap_2020.pdf<\/a><\/p>\n

<\/span>\u984c\u76ee<\/strong>\uff1aDesign and Analysis with Machine Learning-Generated Variables: A Unified Framework for Prediction Bias and the Illusory Sample Size
\n<\/span>
\n\u6982\u8981<\/strong>\uff1aMachine learning (ML) has revolutionized the social sciences by enabling researchers to extract variables from massive unstructured datasets, such as text and images. However, using these ML-predicted variables in downstream statistical analyses leads to substantial bias and invalid inference. To overcome this, we propose a unified statistical framework for valid inference with ML-predicted variables that utilizes a small set of hand-labeled observations. Unlike existing model-specific corrections, our generic framework accommodates predicted outcomes, treatments, or covariates across a wide range of estimators, including linear regression, fixed effects, survival analysis, and instrumental variable estimation. Crucially, we uncover an `illusory sample size<\/span>\u2019\u00a0problem: contrary to common intuition, massive unlabeled datasets do not reduce estimation variance without sufficient hand-labeled data. Accordingly, our framework uses a small pilot dataset to optimize data collection, balancing labeling costs against estimation precision. We demonstrate the framework<\/span>\u2019s utility by revisiting a study on election fraud using ballot image data.<\/p>\n

<\/span>\u767a\u8868\u8a00\u8a9e<\/strong>\uff1a\u65e5\u672c\u8a9e<\/p>\n","protected":false},"excerpt":{"rendered":"

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