Publications by Year: Forthcoming

Forthcoming An Education System with Hierarchical Concept Maps
Michail Schwab, Hendrik Strobelt, James Tompkin, Colin Fredericks, Connor Huff, Dana Higgins, Anton Strezhnev, Mayya Komisarchik, Gary King, and Hanspeter Pfister. Forthcoming. “ An Education System with Hierarchical Concept Maps.” IEEE Transactions on Visualization and Computer Graphics.Abstract

Information hierarchies are difficult to express when real-world space or time constraints force traversing the hierarchy in linear presentations, such as in educational books and classroom courses. We present, which allows linear and non-linear presentation and navigation of educational concepts and material. To support a breadth of material for each concept, is Web based, which allows adding material such as lecture slides, book chapters, videos, and LTIs. A visual interface assists the creation of the needed hierarchical structures. The goals of our system were formed in expert interviews, and we explain how our design meets these goals. We adapt a real-world course into, and perform introductory qualitative evaluation with students.

Edited transcript of a talk on Partisan Symmetry at the 'Redistricting and Representation Forum'
Gary King. Forthcoming. “Edited transcript of a talk on Partisan Symmetry at the 'Redistricting and Representation Forum'.” Bulletin of the American Academy of Arts and Sciences, Winter, Pp. 55-58.Abstract

The origin, meaning, estimation, and application of the concept of partisan symmetry in legislative redistricting, and the justiciability of partisan gerrymandering. An edited transcript of a talk at the “Redistricting and Representation Forum,” American Academy of Arts & Sciences, Cambridge, MA 11/8/2017.

Here also is a video of the original talk.

A Theory of Statistical Inference for Matching Methods in Causal Research
Stefano M. Iacus, Gary King, and Giuseppe Porro. Forthcoming. “A Theory of Statistical Inference for Matching Methods in Causal Research.” Political Analysis.Abstract

Researchers who generate data often optimize efficiency and robustness by choosing stratified over simple random sampling designs. Yet, all theories of inference proposed to justify matching methods are based on simple random sampling. This is all the more troubling because, although these theories require exact matching, most matching applications resort to some form of ex post stratification (on a propensity score, distance metric, or the covariates) to find approximate matches, thus nullifying the statistical properties the theories are designed to ensure. Fortunately, the type of sampling used in a theory of inference is an axiom, rather than an assumption vulnerable to being proven wrong, and so we can replace simple with stratified sampling, so long as we can show, as we do here, that the implications of the theory are coherent and remain true. We also show, under our resulting stratified sampling-based theory of inference, that matching in observational studies becomes intuitive and easy to understand. Properties of estimators based on this theory can be satisfied without asymptotics, assumptions hidden in data analysis rather than stated up front, or unfamiliar estimators. This theory also allows binary, multicategory, and continuous treatment variables from the outset and straightforward   extensions for imperfect treatment assignment and different versions of treatments.