Modelling Network Interference with Multi-valued Treatments: the Causal Effect of Immigration Policy on Crime Rates
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Policy evaluation studies, which aim to assess the effect of an intervention, imply some statistical challenges: real-world scenarios provide treatments which have not been assigned randomly and the analysis might be further complicated by the presence of interference between units. Researchers have started to develop novel methods that allow to manage spillover mechanisms in observational studies, under binary treatments. But many policy evaluation studies require complex treatments, such as multi-valued treatments. For instance, in political sciences, evaluating the impact of policies implemented by administrative entities often implies a multi-valued approach, as the general political stance towards a specific issue varies over many dimensions. In this work, we extend the statistical framework about causal inference under network interference in observational studies, allowing for a multi-valued individual treatment and an interference structure shaped by a weighted network. Under multi-valued treatment, each unit is exposed to all levels of the treatment, due to the influence of his neighbors, according to the network weights. The estimation strategy is based on a joint multiple generalized propensity score and allows to estimate direct effects, controlling for both individual and network covariates. We follow the proposed methodology to analyze the impact of national immigration policy on crime rates. We define a multi-valued characterization of political attitudes towards migrants and we assume that the extent to which each country can be influenced by another is modeled by an appropriate indicator, that we call Interference Compound Index (ICI). Results suggest that implementing highly restrictive immigration policies leads to an increase of crime rates and the magnitude of estimated effects is stronger if we take into account multi-valued interference.
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