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Randomized controlled trials are the gold standard for measuring causaleffects. However, they are often not always feasible, and causal treatmenteffects must be estimated from observational data. Observational studies do notallow robust conclusions about causal relationships unless statisticaltechniques account for the imbalance of pretreatment confounders across groupswhile key assumptions hold. Propensity score and balance weighting (PSBW) areuseful techniques that aim to reduce the imbalances between treatment groups byweighting the groups to look alike on the observed confounders. There are manymethods available to estimate PSBW. However, it is unclear a priori which willachieve the best trade-off between covariate balance and effective sample size.Moreover, it is critical to assess the validity of key assumptions required forrobust estimation of the needed treatment effects, including the overlap and nounmeasured confounding assumptions. We present a step-by-step guide tocovariate balancing strategies, including how to evaluate overlap, obtainestimates of PSBW, check for covariate balance, and assess sensitivity tounobserved confounding. We compare the performance of several estimationmethods using a case study examining the relative effectiveness of substanceuse treatment programs and provide a user-friendly web application that canimplement the proposed steps.

A tutorial comparing different covariate balancing methods with an  application evaluating the causal effects of substance use treatment programs  for adolescents