Causal inference packages for RWE and Observational data - a curated list
Darko Medin
Data Scientist and a Biostatistician. Developer of ML/AI models. Researcher in the fields of Biology and Clinical Research. Helping companies with Digital products, Artificial intelligence, Machine Learning.
You may find the full list at adatascience.com by navigating to Free Resources or directly here https://adatascience.com/causal-inference-packages-in-r-for-rwe-and-observational-data.
The list is curated based on potential use cases for RWE (Real World Evidence) / Observational data.
The fields covered by the packages :
Propensity scores
Inverse probability weighting
Covariate Balanced Propensity scores
Entropy weighting
Matching for Causal inference
Causal inference diagnostics
Instrumental variables
Mendelian Randomization
This edition was based mostly on non-ML approaches. In some of the next edition Causal ML packages will be a dedicated version at 'Advanced Stats and Data Science' and 'ADataScience'. Also next versions will include the advanced Python packages for this field too.
by Darko Medin