To increase the precision of a randomized trial, experimenters often adjust estimates of treatment effects using baseline covariates thought to predict the outcome of interest. In a previous paper, we proved that even under the Neyman-Rubin model, if the covariates and the method for adjustment are determined before randomization, this process can increase precision in a manner quite similar to a comparable blocked experiment. Typically, however, experimenters wish to adjust for the covariates that are most imbalanced between treatment and control, given the realized randomization.

To become viral, online video ads need to be viewed and then shared. Yet, what works for one decision may not work for the other. In this research we propose a novel consumer-centric model of viral advertising consisting of viewing and sharing decisions. We apply the model to assess the role of humor, present in 91% of viral ads, by teasing out the differential impact that type of humor (pure or shocking) has on each decision.

We propose the use of Kernel Regularized Least Squares (KRLS) for social science modeling and inference problems. KRLS borrows from machine learning methods designed to solve regression and classification problems without relying on linearity or additivity assumptions. The method constructs a flexible hypothesis space that uses kernels as radial basis functions and finds the best fitting surface in this space by minimizing a complexity-penalized least squares problem.

Two trends are driving innovation and discovery in biological sciences: technologies that allow holistic surveys of genes, proteins, and metabolites and a realization that biological processes are driven by complex networks of interacting biological molecules.

Identifying interesting relationships between pairs of variables in large data
sets is increasingly important.

In this paper we document that the probability that a product is added to a country’s export basket is, on average, 65% larger if a neighboring country is a successful exporter of that same product. We interpret our result as evidence of international intra-industry knowledge diffusion.

Between 1964 and 1971, more than 750 riots flared up in black neighborhoods across the United States. Scholarship on how the American polity respond to these violent protests is contested. Some scholars argue that urban riots produced a conservative ``backlash'' among white voters, while other scholars find little or no effect.

Traditional single-shot causal inference models investigate the effect of a single action at a single point in time and are an invaluable tool for political scientists. Often, however, actions unfold over time, with political entities reacting to a shifting environment. Accordingly, single-shot methods leave researchers unable to extract meaningful causal inferences about these dynamic processes.

We consider the interaction between members of the executive and backbenchers in the House of Commons between the Great Reform Act and the Great War, a period of radical internal reform that birthed the Westminster system in its current form. We gather new data of over a million speeches in seventeen thousand debates to model the way in which the cabinet-legislative relationship changed over time. In particular, we conceptualize debates as Markov chains moving between speaker states and focus on estimating transition probabilities of the same.

Although the statistical and systematic problems of public opinion polls are fairly widely recognized, we tend to assume that published polling results reflect some sort of actual poll.

Matching is an increasingly popular method of causal inference in observational data, but applications of it are often poorly executed. We address this problem by providing a 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).

Analyses of social network data have suggested that obesity, smoking, happiness, and loneliness all travel through social networks. Individuals exert ‘‘contagion effects’’ on one another through social ties and association. These analyses have come under critique because of the possibility that homophily from unmeasured factors may explain these statistical associations and because similar findings can be obtained when the same methodology is applied to height, acne, and headaches, for which the conclusion of contagion effects seems somewhat less plausible.

Empirical testing of competing theories lies at the heart of social science research. We demonstrate that a very general and well-known class of statistical models, called finite mixture models, provides an effective way of rival theory testing. In the proposed framework, each observation is assumed to be generated from a statistical model implied by one of the theories under consideration. Researchers can then estimate the probability that a specific observation is consistent with either of the competing theories.

We constructed a corpus of digitized texts containing about 4% of all books ever printed. Analysis of this corpus enables us to investigate cultural trends quantitatively. We survey the vast terrain of ‘culturomics,’ focusing on linguistic and cultural phenomena that were reflected in the English language between 1800 and 2000. We show how this approach can provide insights about fields as diverse as lexicography, the evolution of grammar, collective memory, the adoption of technology, the pursuit of fame, censorship, and historical epidemiology.

Can religious leaders use their popular influence to political ends? This paper explores this question using over 700 years of Nile flood data. Results show that deviant Nile floods were related to significant decreases in the probability of change of the highest-ranking religious authority. Qualitative evidence suggests this decrease reflects an increase in political power stemming from famine-induced surges in the religious authority's control over popular support.

Presentation File: 

nilepaper.pdf

Type Ia supernovae (SN Ia) are the most precise cosmological distance indicators and are important for measuring the acceleration of the Universe and the properties of dark energy. To obtain the best distance estimates, the photometric time series (apparent light curves) of SN Ia at multiple wavelengths must be properly modeled. The observed data result from multiple random and uncertain effects, such as measurement error, host galaxy dust extinction and reddening, peculiar velocities, and distances.

Social scientists typically devote considerable effort to reducing measurement error during data collection only to 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 consider four classes of graphical causal models: the Finest Fully Randomized Causally Interpretable Structured Tree Graph (FFRCISTG) of Robins (1986), the agnostic causal model of Spirtes et al. (1993), the Non-Parametric Structural Equation Model (NPSEM) of Pearl (2000), and the Minimal Counterfactual Model (MCM) which we introduce.

Presentation File: 

wp100.pdf

During the last decade, the science of networks has grown into an enormous interdisciplinary endeavor, with methods and applications drawn from across the natural, social, and information sciences. One of the most important and prominent ideas from network science is the algorithmic detection of tightly-connected groups of nodes known as communities. Here we develop a formulation to detect communities in a very broad setting by studying general dynamical processes on networks.