Title: Matching Methods for Reducing Data Imbalance
Abstract: Causal Inference allows researchers to draw cause-and-effect conclusions for experimental and observational data. However, when data arise in observational settings, causal inference becomes more complex requiring different assumptions and data analysis strategies to yield valid conclusions. In the absence of controlled and randomized treatment assignment, matching methods are often used to increase the similarity in the joint and marginal covariate distributions between a treated and an untreated sample to reduce the difference in the observed response of interest due to differences in covariates.
