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I study a regression model in which one covariate is an unknown function of a latent driver of link formation in a network. Rather than specify or fit a parametric network formation model, I introduce a new method based on matching pairs of agents with similar columns of the squared adjacency matrix, the ijth entry of which contains the number of other agents linked to both agents i and j. The intuition behind this approach is that for a large class of network formation models the columns of this matrix characterize all of the identifiable information about individual linking behavior. In the paper, I first describe the model and formalize this intuition. I then introduce estimators for the parameters of the regression model and characterize their large sample properties.

We propose a new unified framework for causal inference when outcomes depend on how agents are linked in a social or economic network. Such network interference describes a large literature on treatment spillovers, social interactions, social learning, information diffusion, social capital formation, and more. Our approach works by first characterizing how an agent is linked in the network using the configuration of other agents and connections nearby as measured by path distance. The impact of a policy or treatment assignment is then learned by pooling outcome data across similarly configured agents. In the paper, we propose a new nonparametric modeling approach and consider two applications to causal inference. The first application is to testing policy irrelevance/no treatment effects. The second application is to estimating policy effects/treatment response. We conclude by evaluating the finite-sample properties of our estimation and inference procedures via simulation.

How can one determine whether a community-level treatment, such as the introduction of a social program or trade shock, alters agents' incentives to form links in a network? This paper proposes analogues of a two-sample Kolmogorov-Smirnov test, widely used in the literature to test the null hypothesis of "no treatment effects," for network data. It first specifies a testing problem in which the null hypothesis is that two networks are drawn from the same random graph model. It then describes two randomization tests based on the magnitude of the difference between the networks' adjacency matrices as measured by the 2 → 2 and ∞ → 1 operator norms. Power properties of the tests are examined analytically, in simulation, and through two real-world applications. A key finding is that the test based on the ∞ → 1 norm can be substantially more powerful than that based on the 2 → 2 norm for the kinds of sparse and degree-heterogeneous networks common in economics.

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The Metropolitan Water Reclamation District (MWRD) of Greater Chicago's Stickney Water Reclamation Plant (SWRP) anaerobically digests approximately 430 dry tons per day (dtpd) (390 dry metric tons per day) of solids and produces 3.4 million ft(3)/day (96 thousand m(3)/day) of biogas from the anaerobic digesters, making it one of the largest municipal digester gas complexes in the world. Installation of new treatment processes, as well as future increases in flows and loads to the plant, are expected to significantly increase production of biologically degradable sludge and biogas. This paper presents a comprehensive planning study that was completed to identify and evaluate alternatives for utilization of this biogas. The best, sustainable approach was identified, taking into consideration economics, social impacts, and environmental impacts. The model results indicate that the most economically favorable scenario involves installing a cogeneration facility to produce electricity on-site, and operating it in conjunction with the plant's existing boilers to satisfy the heating needs of the plant. This scenario also provides the greatest reduction in GHG offsets at the power plants.

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