Date of Award
Master of Science (MS)
Computational and Data Sciences
Erik Linstead, Ph.D.
Cyril Rakovski, Ph.D.
Elizabeth Stevens, Ph.D.
Free and open source software for statistical modeling and machine learning have advanced productivity in data science significantly. Packages such as SciPy in Python and caret in R provide fundamental tools for statistical modeling and machine learning in the two most popular programming languages used by data scientists. Unfortunately, robust tools similar to these are limited in terms of causal inference. The tools in R that exist lack consistent and standardized methodologies and inputs. R lacks a comprehensive package that offers traditional causal inference methods such as standardization, IP weighting, G-estimation, outcome regression, and propensity matching in one common package. CausalModels is meant to fill the gap in open source software concerning causal inference. It offers tools for these methods while accounting for biases in observational data without requiring extensive statistical knowledge from the user. For the purposes of this thesis, CausalModels creates a foundation by implementing popular fundamental methods and excludes more advanced methods that may be added over time.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
J.W. Anderson, "CausalModels: An R Library for Estimating Causal Effects," M. S. thesis, Chapman University, Orange, CA, 2022. https://doi.org/10.36837/chapman.000379
Available for download on Sunday, April 28, 2024