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The underlying causal network from global dyadic events: allies and rivals in international relations

The ever-increasing amount of web information usually leads to more discovery of implicit relations using data-driven methods. While country-country relations are not exactly new for human learning, we illustrate that one can extract the knowledge of such sophisticated relations solely from data. Using the global dyadic events data that record the involving parties and timestamp, we identify the underlying causal network with common initiators or reactors, as well as the nature of pairwise relations as a counterfactual reasoning problem, thereby deriving the significance of causal effects in the network. To formalize the discovery process, we first validate some country-country relations in a small-world network structure after exploitative runs over the entire data set, suggesting the traditional allies and rivals as we know in the real world. Further, we use sentimental analysis based on Wikipedia text data to match the significance of bilateral relations in the causal discovery. Our results show that using time series data of dyadic event counts alone, combined with lexical-based validation, can extract the knowledge of bilateral and multilateral relations and has potential to be a means to measuring and monitoring the climate of complex geopolitics.