CoBWeb: a user-friendly web application to estimate causal treatment effects from observational data using multiple algorithms

12/09/2021
by   Andreas Markoulidakis, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/19/2021

How balance and sample size impact bias in the estimation of causal treatment effects: A simulation study

Observational studies are often used to understand relationships between...
research
04/07/2022

A tutorial for using propensity score weighting for moderation analysis: an application to smoking disparities among LGB adults

Objective. To provide step-by-step guidance and STATA and R code for usi...
research
06/30/2023

Leveraging Observational Data for Efficient CATE Estimation in Randomized Controlled Trials

Randomized controlled trials (RCTs) are the gold standard for causal inf...
research
10/10/2020

Combining Observational and Experimental Data Using First-stage Covariates

Randomized controlled trials generate experimental variation that can cr...
research
02/08/2023

Estimating Longitudinal Causal Effects with Unobserved Noncompliance Using a Semi-Parametric G-computation Algorithm

Participant noncompliance, in which participants do not follow their ass...

Please sign up or login with your details

Forgot password? Click here to reset