Causal inference is the process of determining whether an observed association between phenomena is a true cause and effect relationship.  These methods allow researchers to investigate if the observed outcome of a study was truly caused by the exposure of interest, or whether it occurred due to chance or outside confounding factors. This is especially important in contexts such as observational studies, where the identification and estimation of causal effects is not possible with traditional methods. Causal inference methods specialize in adjusting for confounders, selection bias, and measurement error which complicate causal interpretations in studies of non-randomized interventions and exposures. In our current work, we use this approach to account for the nonrandomized treatment assignment in the presence of interference with complex dependencies and develop more precise estimates of effects.