Gary King

Harvard University
https://gking.harvard.edu
Publications266
h-index86
Citations55,510
Highly Influential Citations2,973
Author pages are created from data sourced from our academic publisher partnerships and public sources.

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  • Publications
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Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference
TLDR
A unified approach is proposed that makes it possible for researchers to preprocess data with matching and then to apply the best parametric techniques they would have used anyway and this procedure makes parametric models produce more accurate and considerably less model-dependent causal inferences.
View on Cambridge Press
Logistic Regression in Rare Events Data
TLDR
It is shown that more efficient sampling designs exist for making valid inferences, such as sampling all available events and a tiny fraction of nonevents, which enables scholars to save as much as 99% of their (nonfixed) data collection costs or to collect much more meaningful explanatory variables.
View on Cambridge Press
Causal Inference without Balance Checking: Coarsened Exact Matching
TLDR
It is shown that CEM possesses a wide range of statistical properties not available in most other matching methods but is at the same time exceptionally easy to comprehend and use.
View on Cambridge Press
Designing Social Inquiry: Scientific Inference in Qualitative Research.
While heated arguments between practitioners of qualitative and quantitative research have begun to test the very integrity of the social sciences, Gary King, Robert Keohane, and Sidney Verba have…
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MatchIt: Nonparametric Preprocessing for Parametric Causal Inference
TLDR
MatchIt implements a wide range of sophisticated matching methods, making it possible to greatly reduce the dependence of causal inferences on hard-to-justify, but commonly made, statistical modeling assumptions.
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Amelia II: A Program for Missing Data
TLDR
The Amelia II package implements a new expectation-maximization with bootstrapping algorithm that works faster, with larger numbers of variables, and is far easier to use, than various Markov chain Monte Carlo approaches, but gives essentially the same answers.
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Making the Most Of Statistical Analyses: Improving Interpretation and Presentation
TLDR
This article offers an approach, built on the technique of statistical simulation, to extract the currently overlooked information from any statistical method and to interpret and present it in a reader-friendly manner.
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Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation
TLDR
This work adapts an algorithm and uses it to implement a general-purpose, multiple imputation model for missing data that is considerably faster and easier to use than the leading method recommended in the statistics literature.
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Enhancing the Validity and Cross-Cultural Comparability of Measurement in Survey Research
We address two long-standing survey research problems: measuring complicated concepts, such as political freedom and efficacy, that researchers define best with reference to examples; and what to do…
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Cem: Coarsened Exact Matching in Stata
TLDR
A Stata implementation of coarsened exact matching, a new method for improving the estimation of causal effects by reducing imbalance in covariates between treated and control groups, is introduced.
View on SAGE
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