Michael J. Higgins

• Kansas State University

We propose a model of treatment interference where the response of a unit depends only on its treatment status and the statuses of units within its K-neighborhood. Current methods for detecting interference include carefully designed randomized experiments and conditional randomization tests on a set of focal units. We give guidance on how to choos...

This research revisits the perennial policy concern that operating subsidies hamper transit efficiency. We argue that the relationship between subsidies and efficiency can be better understood at the regional level and propose improved metrics related to transit efficiency. To begin, we focus on the impact of subsidies on transitsheds rather than t...

Considerable recent work has focused on methods for analyzing experiments which exhibit treatment interference -- that is, when the treatment status of one unit may affect the response of another unit. Such settings are common in experiments on social networks. We consider a model of treatment interference -- the K-nearest neighbors interference mo...

We propose a model of treatment interference where the response of a unit depends only on its treatment status and the statuses of units within its K-neighborhood. Current methods for detecting interference include carefully designed randomized experiments and conditional randomization tests on a set of focal units. We give guidance on how to choos...

Among plausible causes for replicability failure, one that has not received sufficient attention is the environment in which the research is conducted. Consisting of the population, equipment, personnel, and various conditions such as location, time, and weather, the research environment can affect treatments and outcomes, and changes in the resear...

A distinguishing property of communities in networks is that cycles are more prevalent within communities than across communities. Thus, the detection of these communities may be aided through the incorporation of measures of the local ”richness” of the cyclic structure. In this paper, we introduce renewal non-backtracking random walks (RNBRW) as a...

Nitrogen oxides and ozone impact air quality in many parts of the United States, Europe, China, and many other countries. The greatest air quality challenge in Los Angeles, some other areas of California, and some parts of China is to reduce ozone levels to meet regulations. Background ozone is a major factor which makes it more difficult to reduce...

Legislative redistricting is a critical element of representative democracy. A number of political scientists have used simulation methods to sample redistricting plans under various constraints in order to assess their impact on partisanship and other aspects of representation. However, while many optimization algorithms have been proposed, surpri...

In a cluster-randomized experiment, treatment is assigned to clusters of individual units of interest--households, classrooms, villages, etc.--instead of the units themselves. The number of clusters sampled and the number of units sampled within each cluster is typically restricted by a budget constraint. Previous analysis of cluster randomized exp...

A distinguishing property of communities in networks is that cycles are more prevalent within communities than across communities. Thus, the detection of these communities may be aided through the incorporation of measures of the local "richness" of the cyclic structure. In this paper, we introduce renewal non-backtracking random walks (RNBRW) as a...

As the size $n$ of datasets become massive, many commonly-used clustering algorithms (for example, $k$-means or hierarchical agglomerative clustering (HAC) require prohibitive computational cost and memory. In this paper, we propose a solution to these clustering problems by extending threshold clustering (TC) to problems of instance selection. TC...

The environment in which an experiment is conducted is unique to each experiment. While the statistical inferences that are drawn from the analysis of experimental data apply only to the environment in which the experiment is conducted, it is almost always the intent of the researcher to apply the results more broadly. The questions then become, wi...

A major concern in the social sciences is lack of replication of previous studies. An important methodological concern in the social sciences is the ability to determine effect sizes in addition to statistical significance levels. Effect sizes cannot be easily calculated in the absence of sufficient data; usually standard deviations are needed. If...

A distinguishing property of communities in networks is that cycles are more prevalent within communities than across communities. Hence, the detection of these communities may be aided through the use of measures of the local "richness" of cyclic structures. We investigate the use of two methods for quantifying this richness---loop modulus (LM) an...

Matching methods are used to make units comparable on observed characteristics. Full matching can be used to derive optimal matches. However, the method has only been defined in the case of two treatment categories, it places unnecessary restrictions on the matched groups, and existing implementations are computationally intractable in large sample...

Matching is an important tool in causal inference. The method provides a conceptually straightforward way to make groups of units comparable on observed characteristics. The use of the method is, however, limited to situations where the study design is fairly simple and the sample is moderately sized. We illustrate the issue by revisiting a large-s...

Inferences from randomized experiments can be improved by blocking: assigning treatment in fixed proportions within groups of similar units. However, the use of the method is limited by the difficulty in deriving these groups. Current blocking methods are restricted to special cases or run in exponential time; are not sensitive to clustering of dat...

Occasionally, scientific reports have omitted information on standard deviations, making estimates of effect sizes very difficult to impossible. In such situations, several scholars have recommended obtaining an estimate of the standard deviation of distributions by dividing the range of the distribution (highest value minus lowest value) by four....

We derive the variances of estimators for sample average treatment effects under the Neyman-Rubin potential outcomes model for arbitrary blocking assignments and an arbitrary number of treatments.

Vote-tabulation audits can be used to collect evidence that the set of winners (the outcome) of an election ac-cording to the machine count is correct—that it agrees with the outcome that a full hand count of the audit trail would show. The strength of evidence is measured by the p-value of the hypothesis that the machine outcome is wrong. Smaller...