Network Inference in Public Administration: Questions, Challenges, and Models of Causality
Travis A. Whetsell, Michael D. Siciliano
TL;DR
This paper tackles the scarcity of causal evidence for network interventions in public administration by surveying cross-disciplinary network-intervention literature and outlining a five-model typology for network inference. It clarifies causal questions across network, node, and dyadic levels and highlights major challenges such as selection bias, non-independence, and SUTVA/spillovers. By detailing data-collection methods (e.g., name-generation, roster, documentary data) and causal designs (natural, field, and laboratory network approaches), it provides a practical guide for designing and evaluating network-based public interventions. The work aims to bridge public administration and network-science causal inference, enabling researchers to design rigorous studies that reveal what works in network governance and improve policy outcomes.
Abstract
Descriptive and inferential social network analysis has become common in public administration studies of network governance and management. A large literature has developed in two broad categories: antecedents of network structure, and network effects and outcomes. A new topic is emerging on network interventions that applies knowledge of network formation and effects to actively intervene in the social context of interaction. Yet, the question remains how might scholars deploy and determine the impact of network interventions. Inferential network analysis has primarily focused on statistical simulations of network distributions to produce probability estimates on parameters of interest in observed networks, e.g. ERGMs. There is less attention to design elements for causal inference in the network context, such as experimental interventions, randomization, control and comparison networks, and spillovers. We advance a number of important questions for network research, examine important inferential challenges and other issues related to inference in networks, and focus on a set of possible network inference models. We categorize models of network inference into (i) observational studies of networks, using descriptive and stochastic methods that lack intervention, randomization, or comparison networks; (ii) simulation studies that leverage computational resources for generating inference; (iii) natural network experiments, with unintentional network-based interventions; (iv) network field experiments, with designed interventions accompanied by comparison networks; and (v) laboratory experiments that design and implement randomization to treatment and control networks. The article offers a guide to network researchers interested in questions, challenges, and models of inference for network analysis in public administration.