  Background/aims: While randomized controlled trials are the gold standard for
measuring causal effects, robust conclusions about causal relationships can be
obtained using data from observational studies if proper statistical techniques
are used to account for the imbalance of pretreatment confounders across
groups. Propensity score (PS) and balance weighting are useful techniques that
aim to reduce the observed imbalances between treatment groups by weighting the
groups to be as similar as possible with respect to observed confounders.
Methods: We have created CoBWeb, a free and easy-to-use web application for the
estimation of causal treatment effects from observational data, using PS and
balancing weights to control for confounding bias. CoBWeb uses multiple
algorithms to estimate the PS and balancing weights, to allow for more flexible
relations between the treatment indicator and the observed confounders (as
different algorithms make different (or no) assumptions about the structural
relationship between the treatment covariate and the confounders). The optimal
algorithm can be chosen by selecting the one that achieves the best trade-off
between balance and effective sample size. Results: CoBWeb follows all the key
steps required for robust estimation of the causal treatment effect from
observational study data and includes sensitivity analysis of the potential
impact of unobserved confounders. We illustrate the practical use of the app
using a dataset derived from a study of an intervention for adolescents with
substance use disorder, which is available for users within the app
environment. Conclusion: CoBWeb is intended to enable non-specialists to
understand and apply all the key steps required to perform robust estimation of
causal treatment effects using observational data.
