Software

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 obvious 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 has officially been “Qualified for Scientific Use” by the U.S. Food and Drug Administration.

YourCast is (open source and free) software that makes forecasts by running sets of linear regressions together in a variety of sophisticated ways. YourCast avoids the bias that results when stacking datasets from separate cross-sections and assuming constant parameters, and the inefficiency that results from running independent regressions in each cross-section.

The models enable you to have different covariates, or the same covariates with different meanings, in each cross-section. You may choose from a wide variety of smoothing techniques, such as assuming that the separate time series regressions in neighboring (or “similar”) countries are alike, based on similarities in the coefficients (as in other approaches) or in the values or trends in the expected value of the dependent variable. The model works with time-series-cross-sectional data but also data for which the time series varies over more than one cross-section such as log-mortality over time by age, country, sex, and cause. YourCast implements the methods introduced in Federico Girosi and Gary King’s book manuscript on Demographic Forecasting, Princeton University Press.

This software is no longer being actively updated. Previous versions and information about the software are archived here.

A stand-alone, easy-to-use program for running event count and duration regression models, developed by and/or discussed in a series of journal articles by Gary King. (Event count models have a dependent variable measured as the number of times something happens, such as the number of uncontested seats per state or the number of wars per year. Duration models explain dependent variables measured as the time until some event, such as the number of months a parliamentary cabinet endures.) Winner of the APSA Research Software Award.

This software is no longer being actively updated. Previous versions and information about the software are archived here.

A set of Gauss programs and datasets (annotated for pedagogical purposes) to implement many of the maximum likelihood-based statistical models discussed in Gary King’s book Unifying Political Methodology: The Likelihood Theory of Statistical Inference (University of Michigan Press). All datasets are real, not simulated.