  Randomized controlled trials are the gold standard for measuring causal
effects. However, they are often not always feasible, and causal treatment
effects must be estimated from observational data. Observational studies do not
allow robust conclusions about causal relationships unless statistical
techniques account for the imbalance of pretreatment confounders across groups
while key assumptions hold. Propensity score and balance weighting (PSBW) are
useful techniques that aim to reduce the imbalances between treatment groups by
weighting the groups to look alike on the observed confounders. There are many
methods available to estimate PSBW. However, it is unclear a priori which will
achieve the best trade-off between covariate balance and effective sample size.
Moreover, it is critical to assess the validity of key assumptions required for
robust estimation of the needed treatment effects, including the overlap and no
unmeasured confounding assumptions. We present a step-by-step guide to
covariate balancing strategies, including how to evaluate overlap, obtain
estimates of PSBW, check for covariate balance, and assess sensitivity to
unobserved confounding. We compare the performance of several estimation
methods using a case study examining the relative effectiveness of substance
use treatment programs and provide a user-friendly web application that can
implement the proposed steps.
