Estimation of causal effects of binary treatments in unconfounded studies with one continuous covariate.
The estimation of causal effects in nonrandomized studies should comprise two distinct phases: design, with no outcome data available; and analysis of the outcome data according to a specified protocol. Here, we review and compare point and interval estimates of common statistical procedures for estimating causal effects (i.e. matching, subclassification, weighting, and model-based adjustment) with a scalar continuous covariate and a scalar continuous outcome. We show, using an extensive simulation, that some highly advocated methods have poor operating characteristics. In many conditions, matching for the point estimate combined with within-group matching for sampling variance estimation, with or without covariance adjustment, appears to be the most efficient valid method of those evaluated. These results provide new conclusions and advice regarding the merits of currently used procedures.
['Gutman|R|R|', 'Rubin|D B|DB|']
D018849:Controlled Clinical Trials as Topic D006801:Humans D016014:Linear Models D016896:Treatment Outcome
matching Causal inference Rubin causal model regression adjustment spline