Measuring Group Differences in High‐Dimensional Choices: Method and Application to Congressional Speech
研究了选择集维度高时测量群体差异的问题，提出一种利用机器学习纠正有限样本偏差的估计量，并应用于1873-2016年美国国会演讲，发现党派性在1990年代初急剧上升。
We study the problem of measuring group differences in choices when the dimensionality of the choice set is large. We show that standard approaches suffer from a severe finite‐sample bias, and we propose an estimator that applies recent advances in machine learning to address this bias. We apply this method to measure trends in the partisanship of congressional speech from 1873 to 2016, defining partisanship to be the ease with which an observer could infer a congressperson's party from a single utterance. Our estimates imply that partisanship is far greater in recent years than in the past, and that it increased sharply in the early 1990s after remaining low and relatively constant over the preceding century.