Keywords: causal inference, matching, multiple treatments, generalized propensity score, observational data
Randomized clinical trials (RCTs) are ideal for estimating causal effects, as treatment groups are guaranteed to be similar in terms of background covariates. When estimating causal effects using observational data, matching is a commonly used method to replicate the covariate balance achieved in a RCT. Matching algorithms have a rich history dating back to the mid-1900s, but have been used mostly to estimate causal effects between two treatment groups. When there are more than two treatments, estimating causal effects requires additional assumptions and techniques. We propose matching algorithms that address the drawbacks of the current methods, and we use simulations to compare current and new methods. All of the methods display better covariate balance in the matched sets than in the pre-matched cohorts. In addition, we provide advice to investigators on which matching algorithms are preferred for different covariate distributions.