This software implements the method described in Aaron Kaufman, Gary King, and Mayya Komisarchik. Forthcoming. “How to Measure Legislative District Compactness If You Only Know it When You See It.” American Journal of Political Science. Copy at http://j.mp/2u9OWrG
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. In contrast, academics have shown that compactness has multiple dimensions and have generated many conflicting measures. We hypothesize that both are correct -- that compactness is complex and multidimensional, but a common understanding exists across people. We develop a survey to elicit this understanding, with high reliability (in data where the standard paired comparisons approach fails). 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 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.
An 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). 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. 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. Automatic differentiation, stochastic gradient descent, and batch re-normalization are used to carry out the optimization. Other pre-processing functions are available, as well as an interface to the earlier version of the algorithm for comparison. The package also provides users with the ability to extract the generated features for use in other tasks.
(Here's the abstract from our paper: Computer scientists and statisticians are often interested in classifying textual documents into chosen categories. Social scientists and others are often less interested in any one document and instead try to estimate the proportion falling in each category. 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. 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. 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. 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. We evaluate our approach in analyses of a diverse collection of 73 data sets, showing that it substantially improves performance compared to existing approaches. As a companion to this paper, we offer easy-to-use software that implements all ideas discussed herein.)
The RobustSE package implements the generalized information matrix (GIM) test to detect model misspecification described by King & Roberts (2015).
When a researcher suspects a model may be misspecified, rather than attempting to correct by fitting robust standard errors, the GIM test should be utilized as a formal statistical test for model misspecification. If the GIM test rejects the null hypothesis, the researcher should re-specify the model, as it is possible estimators of the misspecified model will be biased.
MatchingFrontier is an easy-to-use R Package for making optimal causal inferences from observational data. Despite their popularity, existing matching approaches leave researchers with two fundamental tensions. 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). Second, they lack a principled solution to revealing the implicit bias-variance trade off: matching methods need to optimize with respect to both imbalance (between the treated and control groups) and the number of observations pruned, but existing approaches optimize with respect to only one; users then either ignore the other, or tweak it, usually suboptimally, by hand.
MatchingFrontier resolves both tensions by consolidating previous techniques into a single, optimal, and flexible approach. It calculates the matching solution with maximum balance for each possible sample size (N, N-1, N-2,...). It thus directly calculates the entire balance-sample size frontier, from which the user can easily choose one, several, or all subsamples from which to conduct their final analysis, given their own choice of imbalance metric and quantity of interest. MatchingFrontier solves the joint optimization problem in one run, automatically, without manual tweaking, and without iteration. Although for each subset size k, there exist a huge (N choose k) number of unique subsets, MatchingFrontier includes specially designed fast algorithms that give the optimal answer, usually in a few minutes.
MatchingFrontier implements the methods in this paper:
- 1 of 4