Validation of Estimates of Treatment Effects in Observational Data
Visualizing validation results according to four steps, namely, set-se...
Generating RCT data or observational data for the examples used in the...
Validation of estimates of conditional average treatment effects in ob...
Generating the synthetic RCT data given marginal distribution of each ...
Replicate treatment effect estimates obtained from a randomized contro...
Estimating the weighted conditional average treatment effects in `sour...
Estimating conditional average treatment effects
Validates estimates of (conditional) average treatment effects obtained using observational data by a) making it easy to obtain and visualize estimates derived using a large variety of methods (G-computation, inverse propensity score weighting, etc.), and b) ensuring that estimates are easily compared to a gold standard (i.e., estimates derived from randomized controlled trials). 'RCTrep' offers a generic protocol for treatment effect validation based on four simple steps, namely, set-selection, estimation, diagnosis, and validation. 'RCTrep' provides a simple dashboard to review the obtained results. The validation approach is introduced by Shen, L., Geleijnse, G. and Kaptein, M. (2023) <doi:10.21203/rs.3.rs-2559287/v2>.