4/9/2023 0 Comments Harvard software inmr![]() First, they are designed to maximize one metric (such as propensity score or Mahalanobis distance) but are judged against another for which they were not designed (such as L1 or differences in means). Despite their popularity, existing matching approaches leave researchers with two fundamental tensions. MatchingFrontier is an easy-to-use R Package for making optimal causal inferences from observational data. As a companion to this paper, we offer easy-to-use software that implements all ideas discussed herein.) We evaluate our approach in analyses of a diverse collection of 73 data sets, showing that it substantially improves performance compared to existing approaches. We develop an improved direct estimation approach without these problems by introducing continuously valued text features optimized for this problem, along with a form of matching adapted from the causal inference literature. Direct estimation avoids these problems, but can suffer when the meaning and usage of language is too similar across categories or too different between training and test sets. Unfortunately, classify and count methods can sometimes be highly model dependent or generate more bias in the proportions even as the percent correctly classified increases. The two existing types of techniques for estimating these category proportions are parametric "classify and count" methods and "direct" nonparametric estimation of category proportions without an individual classification step. Social scientists and others are often less interested in any one document and instead try to estimate the proportion falling in each category. ( Here's the abstract from our paper: Computer scientists and statisticians are often interested in classifying textual documents into chosen categories. The package also provides users with the ability to extract the generated features for use in other tasks. Other pre-processing functions are available, as well as an interface to the earlier version of the algorithm for comparison. Automatic differentiation, stochastic gradient descent, and batch re-normalization are used to carry out the optimization. This version of the software refines the original method by implementing a technique for selecitng optimal textual features in order to minimize the error of the estimated category proportions. This method is meant to improve on the ideas in Hopkins and King (2010), which introduced a quantification algorithm to estimate category proportions without directly classifying individual observations. We also offer compactness data from our validated measure for 20,160 state legislative and congressional districts, as well as software to compute this measure from any district.Īn R package for estimating category proportions in an unlabeled set of documents given a labeled set, by implementing the method described in Jerzak, King, and Strezhnev (2019). We create a statistical model that predicts, with high accuracy, solely from the geometric features of the district, compactness evaluations by judges and public officials responsible for redistricting, among others. We develop a survey to elicit this understanding, with high reliability (in data where the standard paired comparisons approach fails). We hypothesize that both are correct - that compactness is complex and multidimensional, but a common understanding exists across people. In contrast, academics have shown that compactness has multiple dimensions and have generated many conflicting measures. Our paper abstract: To deter gerrymandering, many state constitutions require legislative districts to be "compact." Yet, the law offers few precise definitions other than "you know it when you see it," which effectively implies a common understanding of the concept. ![]() “How to Measure Legislative District Compactness If You Only Know it When You See It.” American Journal of Political Science. This software implements the method described in Aaron Kaufman, Gary King, and Mayya Komisarchik.
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