HDPS

High Dimensional Propensity Score A Python package with the functionality to use the algorithm explained in https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3077219/

References: [1] Schneeweiss, Sebastian & Rassen, Jeremy & Glynn, Robert & Avorn, Jerry & Mogun, Helen & Brookhart, M. (2009). High-Dimensional Propensity Score Adjustment in Studies of Treatment Effects Using Health Care Claims Data. Epidemiology (Cambridge, Mass.). 20. 512-22. 10.1097/EDE.0b013e3181a663cc. [2] Sam Lendle, "lendle/hdps: High-dimensional propensity score algorithm". link: https://rdrr.io/github/lendle/hdps/ [3] ohn Tazare & Ian Douglas & Elizabeth Williamson, 2019. "hdps: Implementation of high-dimensional propensity score approaches in Stata," London Stata Conference 2019 05, Stata Users Group. Link: https://www.stata.com/meeting/uk19/slides/uk19_tazare.pdf [4] Schneeweiss S. Automated data-adaptive analytics for electronic healthcare data to study causal treatment effects. Clin Epidemiol. 2018;10:771-788. Published 2018 Jul 6. doi:10.2147/CLEP.S166545 [5] Wyss R, Fireman B, Rassen JA, Schneeweiss S. Erratum: High-dimensional Propensity Score Adjustment in Studies of Treatment Effects Using Health Care Claims Data. Epidemiology. 2018 Nov;29(6):e63-e64. doi: 10.1097/EDE.0000000000000886. Erratum for: Epidemiology. 2009 Jul;20(4):512-22. PMID: 29958191.

Unit testing references:

• https://microsoft.github.io/code-with-engineering-playbook/machine-learning/ml-testing/
• https://eugeneyan.com/writing/testing-ml/
• https://github.com/CoreyMSchafer/code_snippets/tree/master/Python-Unit-Testing.

Thanks: Thanks to the following resources which helped to understand python, packages usage and other relevant concepts and to prepare code for this algorithm. a. https://www.python.org/ b. https://pandas.pydata.org/ c. https://numpy.org/ d. https://www.youtube.com/c/Coreyms