Shahryar Minhas bio photo

Shahryar Minhas

Assistant Professor, Michigan State University

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  • Inferential Approaches for Network Analysis: AMEN for Latent Factor Models – with Peter D. Hoff and Michael D. Ward. Revise & Resubmit at Political Analysis.
    • We introduce a Bayesian approach to conduct inferential analyses on dyadic data while accounting for interdependencies between observations through a set of additive and multiplicative effects (AME). The AME model is built on a generalized linear modeling framework and is thus flexible enough to be applied to a variety of contexts. We contrast the AME model to two prominent approaches in the literature: the latent space model (LSM) and the exponential random graph model (ERGM). Relative to these approaches, we show that the AME approach is a) to be easy to implement; b) interpretable in a general linear model framework; c) computationally straightforward; d) not prone to degeneracy; e) captures 1st, 2nd, and 3rd order network dependencies; and f) notably outperforms ERGMs and LSMs on a variety of metrics and in an out-of-sample context. In summary, AME offers a straightforward way to undertake nuanced, principled inferential network analysis for a wide range of social science questions.


  • Multiple Imputation Using Gaussian Copulas – with Florian Hollenbach, Iavor Bojinov, Nils W. Metternich, Michael D. Ward, and Alexander Volfovsky. Revise & Resubmit at Sociological Methods & Research.
    • Missing observations are pervasive throughout empirical research, especially in the social sciences. Despite multiple approaches to dealing adequately with missing data, many scholars still fail to address this vital issue. In this paper, we present a simple-to-use method for generating multiple imputations using a Gaussian copula. The Gaussian copula for multiple imputation Hoff (2007) allows scholars to attain estimation results that have good coverage and small bias. The use of copulas to model the dependence among variables will enable researchers to construct valid joint distributions of the data, even without knowledge of the actual underlying marginal distributions. Multiple imputations are then generated by drawing observations from the resulting posterior joint distribution and replacing the missing values. Using simulated and observational data from published social science research, we compare imputation via Gaussian copulas with two other widely used imputation methods: MICE and Amelia II. Our results suggest that the Gaussian copula approach has a slightly smaller bias, higher coverage rates, and narrower confidence intervals compared to the other methods. This is especially true when the variables with missing data are not normally distributed. These results, combined with theoretical guarantees and ease-of-use suggest that the approach examined provides an attractive alternative for applied researchers undertaking multiple imputations.


  • Predicting Violence: Network Dynamics in Nigeria – with Cassy Dorff and Max Gallop. Revise & Resubmit at Journal of Politics.
    • Canonical studies of civil war examine the conditions that favor conflict across the globe. While predicting the onset of intrastate war has seen great progress, analysis on the occurrence of battles between actors remains obscure. Despite growing interest in the logic of rebel group fighting, empirical assessment of these relationships is underdeveloped. We unmask the interdependent dynamics of civil conflict by conceptualizing actors and battles as nodes and ties in a network. We examine how these relationships change over time and how their evolution in one time period enables precise prediction of conflict in future periods. Our network approach yields theoretical implications for understanding how the entrance of particularly aggressive actors can decisively alter the trajectory of civil conflict.

Select Papers Under Review or In Process


  • Measuring Influence in Tensors of Conflict – with Peter D. Hoff and Michael D. Ward.
    • Measuring influence and determining what drives it are persistent questions in political science and in network analysis more generally. Herein we focus on the domain of international relations. Our major substantive question is: How can we determine what characteristics make an actor influential? To address the topic of influence, we build on a multilinear tensor regression framework (MLTR) that captures influence relationships using a tensor generalization of a vector autoregression model. Influence relationships in that approach are captured in a pair of n x n matrices and provide measurements of how the network actions of one actor may influence the future actions of another. A limitation of the MLTR and earlier latent space approaches is that there are no direct mechanisms through which to explain why a certain actor is more or less influential than others. Our new framework, social influence regression, provides a way to statistically model the influence of one actor on another as a function of characteristics of the actors. Thus we can move beyond just estimating that an actor influences another to understanding why. To highlight the utility of this approach, we apply it to studying monthly-level conflictual events between countries as measured through the Integrated Crisis Early Warning System (ICEWS) event data project.


  • Keeping Friends Close, but Enemies Closer: Foreign Aid Responses to Natural Disasters – with Cindy Cheng.
    • Despite a robust body of evidence that suggests that aid donors are largely motivated by strategic considerations when dispensing aid, anecdotal evidence suggests that countries often receive substantial humanitarian aid following natural disasters, even from strategic opponents. In this paper, we square this seeming incongruity by arguing that natural disasters can serve to, at least temporarily, emphasize the humanitarian, as opposed to the political or economic, aspects of a bilateral relationship. We argue that natural disaster severity increases the likelihood of donors giving humanitarian aid to strategic adversaries in the short term. However, we find that in the long-term, donors are still more likely to pursue strategic self-interest by increasing civil society aid to strategic adversaries following a natural disaster. We test our findings using a new measure of strategic interest which we argue improves upon existing measures by accounting for third-order dependencies in dyadic data. Our findings suggest that social context matters in short-term humanitarian aid allocation. They also have important implications for the future of foreign aid allocations more generally, as the number of natural disasters is likely to rise, not fall, with changing climate conditions.


  • A Latent Factor Approach to Measuring State Preferences – with Max Gallop.
    • State preferences play an important, yet under discussed role in international politics. This is in large part because actually observing and measuring these preferences is impossible. In general, scholars have tried to infer preferences using either UN voting or alliance behavior. The two most notable measures of state preferences that have flowed from this research area are ideal point (Bailey et al., 2015) and S Scores (Signorino & Ritter, 1999). The basis of both these models is a spatial weighting scheme that has proven useful but discounts higher-order effects that might be present in relational data structures such as UN voting and alliances. We begin by arguing that both alliances and UN voting are simply examples of the multiple layers upon which states interact with one another. To estimate a measure of state preferences from this multilayer structure, we introduce a latent factor model that provides a reduced-rank approximation of the main patterns across the layers. Our new measure of preferences plausibly describes important state relations, and yields important insights on the relationship between preferences, democracy, and international conflict. Most importantly, a model of conflict that uses this measure of state preferences decisively outperforms models using extant measures when it comes to predicting conflict in an out of sample context.


  • Diffusion of Antitrust Laws in Open Economies – with Tim Büthe.
    • Antitrust law seeks to prevent the accumulation and abuse of market power by prohibiting cartels, bid-rigging, and similar anticompetitive behavior. A deliberately pro-market policy, antitrust has been shown to result in lower prices, more innovation, and greater efficiency. Antitrust enforcement, however, is inherently political - and has the potential for abuse - in that it entails the use of state power to constrain and possibly redistribute private economic power. Until 1990, antitrust was virtually entirely the domain of advanced capitalist democracies with strong rule-of-law traditions and professional public bureaucracies. Since then, the number of jurisdictions with antitrust laws has grown rapidly, from some thirty to more than one-hundred-and-thirty today. Most of the existing literature on this strikingly rapid diffusion has focused on the domestic conditions that are conducive to the adoption of such laws, such as political and economic liberalization. Even the few papers that consider factors such as international trade or World Bank advocacy of antitrust law adoption, model the enactment of antitrust laws as an independent decision for each country. Such models fail to capture causally important aspects of the way in which antitrust has diffused through the international political-economic system due to interdependencies in state decision-making. To analyze the global diffusion of antitrust empirically, we use an original dataset on not only when states adopted antitrust laws but also their content. This allows us to assess various mechanisms through which antitrust provisions diffuse across borders using an event history framework with spatial lags.