6. Discussion and conclusion
While contagion effects have important implications for both theoretical and empirical studies, they are generally difficult to identify, as influence processes are often entangled with other processes such as selection and environmental factors. Here we show that this entanglement/difficulty can essentially be framed as an omitted variable bias problem, and the methods currently used (e.g. SIENA, propensity score etc.) either do not deal with this problem or require strong assumptions.
In this paper, we propose several alternative estimation methods that have the potential to identify contagion effects when there are omitted variables present, and we use Monte Carlo simulation to test the performance of these estimators. Results show that all three methods proposed generally perform well in terms of recovering the true contagion effects when there is an unobserved variable that codetermines influence and selection. Specifically, a latent space adjustedapproachoutperforms the othermethods that are traditionally used to deal with omitted variable bias problem, including a SEM based approach and an IV based approach: (1) the latent space adjusted approach produces much smaller bias in estimating the lagged dependent variable when T is small and the true coefficient for lagged dependent variable is large; (2) latent space adjusted approach generates slightly smaller bias in estimating the contagion effects than the other two methods in most scenarios; (3) the latent space adjusted approach is generally more efficient than the other methods, especially when T is small.
