Gary King is the Albert J. Weatherhead III University Professor at Harvard University, based in the Department of Government (in the Faculty of Arts and Sciences). He also serves as Director of the Institute for Quantitative Social Science. King and his research group develop and apply empirical methods in many areas of social science research, focusing on innovations that span the range from statistical theory to practical application. For more information, see his bio and curriculum vitae.
Gary King is the Albert J. Weatherhead III University Professor at Harvard University -- one of 24 with the title of University Professor, Harvard's most distinguished faculty position. He is based in the Department of Government (in the Faculty of Arts and Sciences) and serves as Director of the Institute for Quantitative Social Science. King develops and applies empirical methods in many areas of social science research, focusing on innovations that span the range from statistical theory to practical application.
King has been elected Fellow in 6 honorary societies (National Academy of Sciences 2010, American Statistical Association 2009, American Association for the Advancement of Science 2004, American Academy of Arts and Sciences 1998, Society for Political Methodology 2008, and American Academy of Political and Social Science 2004), President of the Society for Political Methodology (1997-1999), and Vice President of the American Political Science Association (2003-2004). He was appointed a Fellow of the Guggenheim Foundation (1994-1995), Visiting Fellow at Oxford (1994), and Senior Science Advisor to the World Health Organization (1998-2003). King has won more than 30 "best of" awards for his work -- including the Career Achievement Award (2010), Warren Miller Prize (2008), McGraw-Hill Award (2006), Durr Award (2005), Gosnell Prize (1999 and 1997), Outstanding Statistical Application Award (2000), Donald Campbell Award (1997), Eulau Award (1995), Mills Award (1993), Pi Sigma Alpha Award (2005, 1998, and 1993), APSA Research Software Award (2005, 1997, 1994, and 1992), Okidata Best Research Software Award (1999), Okidata Best Research Web Site Award (1999), Mendelsohn Excellence in Mentoring Award (2011), among others. His more than 130 journal articles, 20 open source software packages, and 8 books span most aspects of political methodology, many fields of political science, and several other scholarly disciplines.
King's work is widely read across scholarly fields and beyond academia. He was listed as the most cited political scientist of his cohort; among the group of "political scientists who have made the most important theoretical contributions" to the discipline "from its beginnings in the late-19th century to the present"; and on ISI's list of the most highly cited researchers across the social sciences. His work on legislative redistricting has been used in most American states by legislators, judges, lawyers, political parties, minority groups, and private citizens, as well as the U.S. Supreme Court. His work on inferring individual behavior from aggregate data has been used in as many states by these groups, and in many other practical contexts. His contribution to methods for achieving cross-cultural comparability in survey research have been used in surveys in over eighty countries by researchers, governments, and private concerns. King led an evaluation of the Mexican universal health insurance program, which includes the largest randomized health policy experiment to date. The statistical methods and software he developed are used extensively in academia, government, consulting, and private industry. He is a founder, and an inventor of the original technology for, Learning Catalytics (acquired by Pearson) and Crimson Hexagon, among others.
King has had many students and postdocs, many of whom now hold faculty positions at leading universities and companies. He has collaborated with more than seventy scholars, including many of his students, on research for publication. He has served on more than 30 editorial boards; on the governing councils of the American Political Science Association, Inter-university Consortium for Political and Social Research, the Society for Political Methodology, and the Midwest Political Science Association; and on several National Research Council and National Science Foundation panels.
King received a B.A. from SUNY New Paltz (1980) and a Ph.D. from the University of Wisconsin-Madison (1984). His research has been supported by the National Science Foundation, the Centers for Disease Control and Prevention, the World Health Organization, the National Institute of Aging, the Global Forum for Health Research, and centers, corporations, foundations, and other federal agencies.
Proof that previously used estimators of electoral incumbency advantage were biased, and a new unbiased estimator. Also, the first systematic demonstration that constituency service by legislators increases the incumbency advantage.
New designs and statistical methods for large scale policy evaluations; robustness to implementation errors and political interventions, with very high levels of statistical efficiency. Application to the Mexican Seguro Popular De Salud (Universal Health Insurance) Program.
Resolution of the paradox of why polls are so variable over time during presidential campaigns even though the vote outcome is easily predictable before it starts. Also, a resolution of a key controversy over absentee ballots during the 2000 presidential election; and the methodology of small-n research on executives.
New standards, protocols, and software for citing, sharing, analyzing, archiving, preserving, distributing, cataloging, translating, disseminating, naming, verifying, and replicating scholarly research data and analyses. Also includes proposals to improve the norms of data sharing and replication in science.
Methods for coding, analyzing, and forecasting international conflict and state failure. Evidence that the causes of conflict, theorized to be important but often found to be small or ephemeral, are indeed tiny for the vast majority of dyads, but are large, stable, and replicable wherever the ex ante probability of conflict is large.
The definition of partisan symmetry as a standard for fairness in redistricting; methods and software for measuring partisan bias and electoral responsiveness; discussion of U.S. Supreme Court rulings about this work. Evidence that U.S. redistricting reduces bias and increases responsiveness, and that the electoral college is fair; applications to legislatures, primaries, and multiparty systems.
Methods for forecasting mortality rates (overall or for time series data cross-classified by age, sex, country, and cause); estimating mortality rates in areas without vital registration; measuring inequality in risk of death; applications to US mortality, the future of the Social Security, armed conflict, heart failure, and human security.
Methods for detecting and reducing model dependence (i.e., when minor model changes produce substantively different inferences) in inferring casual effects and other counterfactuals. Matching methods; "politically robust" and cluster-randomized experimental designs; causal bias decompositions.
Statistical models to explain or predict how many events occur for each fixed time period, or the time between events. An application to cabinet dissolution in parliamentary democracies which united two previously warring scholarly literature. Other applications to international relations and U.S. Supreme Court appointments.
Inferring individual behavior from group-level data: The first approach to incorporate both unit-level deterministic bounds and cross-unit statistical information, methods for 2x2 and larger tables, Bayesian model averaging, applications to elections, software.
Statistical methods to accommodate missing information in data sets due to scattered unit nonresponse, missing variables, or cell values or variables measured with error. Easy-to-use algorithms and software for multiple imputation and multiple overimputation for surveys, time series, and time series cross-sectional data. Applications to electoral, and other compositional, data.
How the same unified theory of inference underlies quantitative and qualitative research alike; scientific inference when quantification is difficult or impossible; research design; empirical research in legal scholarship.
How to save 99% of your data collection costs; bias corrections for logistic regression in estimating probabilities and causal effects in rare events data; estimating base probabilities or any quantity from case-control data; automated coding of events.
"Anchoring Vignette" methods for when different respondents (perhaps from different cultures, countries, or ethnic groups) understand survey questions in different ways; an approach to developing theoretical definitions of complicated concepts apparently definable only by example (i.e., "you know it when you see it"); how surveys work.
Amelia II is a complete R package for multiple imputation of missing data. The 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. The program also improves imputation models by allowing researchers to put Bayesian priors on individual cell values, thereby including a great deal of potentially valuable and extensive information. It also includes features to accurately impute cross-sectional datasets, individual time series, or sets of time series for diﬀerent cross-sections. A full set of graphical diagnostics are also available. The program is easy to use, and the simplicity of the algorithm makes it far more robust; both a simple command line and extensive graphical user interface are included.
Amelia II software web site
We introduce a method for estimating incidence curves of several co-circulating infectious pathogens, where each infection has its own probabilities of particular symptom profiles. Our deconvolution method utilizes weekly surveillance data on symptoms from a defined population as well as additional data on symptoms from a sample of virologically confirmed infectious episodes. We illustrate this method by numerical simulations and by using data from a survey conducted on the University of Michigan campus. Last, we describe the data needs to make such estimates accurate.
Link to PLoS version
Massive increases in the availability of informative social science data are making dramatic progress possible in analyzing, understanding, and addressing many major societal problems. Yet the same forces pose severe challenges to the scientific infrastructure supporting data sharing, data management, informatics, statistical methodology, and research ethics and policy, and these are collectively holding back progress. I address these changes and challenges and suggest what can be done.
We develop a computer-assisted method for the discovery of insightful conceptualizations, in the form of clusterings (i.e., partitions) of input objects. Each of the numerous fully automated methods of cluster analysis proposed in statistics, computer science, and biology optimize a different objective function. Almost all are well defined, but how to determine before the fact which one, if any, will partition a given set of objects in an "insightful" or "useful" way for a given user is unknown and difficult, if not logically impossible. We develop a metric space of partitions from all existing cluster analysis methods applied to a given data set (along with millions of other solutions we add based on combinations of existing clusterings), and enable a user to explore and interact with it, and quickly reveal or prompt useful or insightful conceptualizations. In addition, although uncommon in unsupervised learning problems, we offer and implement evaluation designs that make our computer-assisted approach vulnerable to being proven suboptimal in specific data types. We demonstrate that our approach facilitates more efficient and insightful discovery of useful information than either expert human coders or many existing fully automated methods.
We highlight common problems in the application of random treatment assignment in large scale program evaluation. Random assignment is the defining feature of modern experimental design. Yet, errors in design, implementation, and analysis often result in real world applications not benefiting from the advantages of randomization. The errors we highlight cover the control of variability, levels of randomization, size of treatment arms, and power to detect causal effects, as well as the many problems that commonly lead to post-treatment bias. We illustrate with an application to the Medicare Health Support evaluation, including recommendations for improving the design and analysis of this and other large scale randomized experiments.
Matching is an increasingly popular method of causal inference in observational data, but following methodological best practices has proven difficult for applied researchers. We address this problem by providing a simple graphical approach for choosing among the numerous possible matching solutions generated by three methods: the venerable ``Mahalanobis Distance Matching'' (MDM), the commonly used ``Propensity Score Matching'' (PSM), and a newer approach called ``Coarsened Exact Matching'' (CEM). In the process of using our approach, we also discover that PSM often approximates random matching, both in many real applications and in data simulated by the processes that fit PSM theory. Moreover, contrary to conventional wisdom, random matching is not benign: it (and thus PSM) can often degrade inferences relative to not matching at all. We find that MDM and CEM do not have this problem, and in practice CEM usually outperforms the other two approaches. However, with our comparative graphical approach and easy-to-follow procedures, focus can be on choosing a matching solution for a particular application, which is what may improve inferences, rather than the particular method used to generate it.
Social scientists typically devote considerable effort to reducing measurement error during data collection and then ignore the issue during data analysis. Although many statistical methods have been proposed for reducing measurement error-induced biases, few have been widely used because of implausible assumptions, high levels of model dependence, difficult computation, or inapplicability with multiple mismeasured variables. We develop an easy-to-use alternative that generalizes the popular multiple imputation (MI) framework by treating missing data problems as a special case of extreme measurement error and correcting for both. Like MI, the proposed "multiple overimputation" (MO) framework is a simple two-step procedure. First, multiple (≈5) completed copies of the data set are created where cells measured without error are held constant, those missing are imputed from the distribution of predicted values, and cells (or entire variables) with measurement error are "overimputed," that is imputed from a predictive distribution with observation-level priors defined by the mismeasured values and available external information, if any. In the second step, analysts can then run whatever statistical method they would have run on each of the overimputed data sets as if there had been no missingness or measurement error; the results are then combined via a simple procedure. We also (will) offer open source software that implements all the methods described herein.
When respondents use the ordinal response categories of standard survey questions in different ways, the validity of analyses based on the resulting data can be biased. Anchoring vignettes is a survey design technique intended to correct for some of these problems. The anchors package in R includes methods for evaluating and choosing anchoring vignettes, and for analyzing the resulting data.
MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007) for improving parametric statistical models by preprocessing data with nonparametric matching methods. 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. The software also easily ts into existing research practices since, after preprocessing data with MatchIt, researchers can use whatever parametric model they would have used without MatchIt, but produce inferences with substantially more robustness and less sensitivity to modeling assumptions. MatchIt is an R program, and also works seamlessly with Zelig.
Population mortality forecasts are widely used for allocating public health expenditures, setting research priorities, and evaluating the viability of public pensions, private pensions, and health care financing systems. In part because existing methods seem to forecast worse when based on more information, most forecasts are still based on simple linear extrapolations that ignore known biological risk factors and other prior information. We adapt a Bayesian hierarchical forecasting model capable of including more known health and demographic information than has previously been possible. This leads to the first age- and sex-specific forecasts of American mortality that simultaneously incorporate, in a formal statistical model, the effects of the recent rapid increase in obesity, the steady decline in tobacco consumption, and the well known patterns of smooth mortality age profiles and time trends. Formally including new information in forecasts can matter a great deal. For example, we estimate an increase in male life expectancy at birth from 76.2 years in 2010 to 79.9 years in 2030, which is 1.8 years greater than the U.S. Social Security Administration projection and 1.5 years more than U.S. Census projection. For females, we estimate more modest gains in life expectancy at birth over the next twenty years from 80.5 years to 81.9 years, which is virtually identical to the Social Security Administration projection and 2.0 years less than U.S. Census projections. We show that these patterns are also likely to greatly affect the aging American population structure. We offer an easy-to-use approach so that researchers can include other sources of information and potentially improve on our forecasts too.
We introduce a new "Monotonic Imbalance Bounding" (MIB) class of matching methods for causal inference with a surprisingly large number of attractive statistical properties. MIB generalizes and extends in several new directions the only existing class, "Equal Percent Bias Reducing" (EPBR), which is designed to satisfy weaker properties and only in expectation. We also offer strategies to obtain specific members of the MIB class, and analyze in more detail a member of this class, called Coarsened Exact Matching, whose properties we analyze from this new perspective. We offer a variety of analytical results and numerical simulations that demonstrate how members of the MIB class can dramatically improve inferences relative to EPBR-based matching methods.
We introduce a new framework for forecasting age-sex-country-cause-specific mortality rates that incorporates considerably more information, and thus has the potential to forecast much better, than any existing approach. Mortality forecasts are used in a wide variety of academic fields, and for global and national health policy making, medical and pharmaceutical research, and social security and retirement planning.
As it turns out, the tools we developed in pursuit of this goal also have broader statistical implications, in addition to their use for forecasting mortality or other variables with similar statistical properties. First, our methods make it possible to include different explanatory variables in a time series regression for each cross-section, while still borrowing strength from one regression to improve the estimation of all. Second, we show that many existing Bayesian (hierarchical and spatial) models with explanatory variables use prior densities that incorrectly formalize prior knowledge. Many demographers and public health researchers have fortuitously avoided this problem so prevalent in other fields by using prior knowledge only as an ex post check on empirical results, but this approach excludes considerable information from their models. We show how to incorporate this demographic knowledge into a model in a statistically appropriate way. Finally, we develop a set of tools useful for developing models with Bayesian priors in the presence of partial prior ignorance. This approach also provides many of the attractive features claimed by the empirical Bayes approach, but fully within the standard Bayesian theory of inference.
Ecological Inference: New Methodological Strategies brings together a diverse group of scholars to survey the latest strategies for solving ecological inference problems in various fields. The last half decade has witnessed an explosion of research in ecological inference – the attempt to infer individual behavior from aggregate data. The uncertainties and the information lost in aggregation make ecological inference one of the most difficult areas of statistical inference, but such inferences are required in many academic fields, as well as by legislatures and the courts in redistricting, by businesses in marketing research, and by governments in policy analysis.
A program for analyzing most any feature of district-level legislative elections data, including prediction, evaluating redistricting plans, estimating counterfactual hypotheses (such as what would happen if a term-limitation amendment were imposed). This implements statistical procedures described in a series of journal articles and has been used during redistricting in many states by judges, partisans, governments, private citizens, and many others. The earlier version was winner of the APSA Research Software Award.
This program multiply imputes missing data in cross-sectional, time series, and time series cross-sectional data sets. It includes a Windows version (no knowledge of R required), and a version that works with R either from the command line or via a GUI.
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.
This is a set of easy-to-use Stata macros that implement the techniques described in Gary King, Michael Tomz, and Jason Wittenberg's "Making the Most of Statistical Analyses: Improving Interpretation and Presentation". To install Clarify, type "net from http://gking.harvard.edu/clarify" at the Stata command line. The documentation [ HTML | PDF ] explains how to do this. We also provide a zip archive for users who want to install Clarify on a computer that is not connected to the internet. Winner of the Okidata Best Research Software Award. Also try -ssc install qsim- to install a wrapper, donated by Fred Wolfe, to automate Clarify's simulation of dummy variables.