Package: CausalGPS 0.5.1

Installation | Usage | Additional parameters | Causal Inference Approach (ci.appr)

Environment Setup | Git Branching Model | Where to submit pull requests? | Pull request checklist | Reporting bugs | Style Guide | Summary | Names | Spaces and Indentation | Other notes | Notes on SuperLearner | Logger

Usage | Technical Details for Matching | Technical Details for Covariate Balance | References

1) How to define a new transformer? | 2) Is the order of transformers important? | 3) How change the logger level? | 4) Is there any trade-off between number of CPU cores (nthread) and memory usage? | 5) I am using macOS, however, I cannot see any performance increase with increasing number of threads (nthread). | 6) I am running the package on HPC; however, I think the package is using only one core. | 7) What is the counter_weight column in the pseudo population? | 8) Is there a public data set that I can test my model? | 9) Can a data sample match with itself? | 10) Where can I get the code? | 11) How does trimming work? | 12) Can I use a data with missing value? | 13) In the matching approach, I realized computation with scale = 1 is faster than any other amount. Is that correct? | 14) Encountering an error while executing the non-parametric exposure-response function: Error in checkForRemoteErrors(val) : one node produced an error: length(xeval) < .maxEvalPts is not TRUE.

Getting the Code | Installing the Package | Cloning the Package | Forking the package | Package development, test, check cycle | Running Examples | Generating Synthetic Data | Estimating GPS Values for the Dataset | Generating Pseudo Population | Further Processing | Steps for using precomputed data during the test

Setting up the environment | Definition File | Building Singularity Image | Run an R Session | Run a Rstudio Session

Available SuperLearner Libraries | Implementation | References

Usage | Technical Details for Data Generating Process