<em>CausalModels</em>: An R Library for Estimating Causal Effects

<p>Free and open source software for statistical modeling and machine learning have advanced productivity in data science significantly. Packages such as <em>SciPy </em>in Python and <em>caret </em>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. <em>CausalModels</em> 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, <em>CausalModels</em> creates a foundation by implementing popular fundamental methods and excludes more advanced methods that may be added over time.</p>

Name thesis:degree_name

Master of Science (MS)

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computational and Data Sciences

Year dc:date.available

2022

Author dc:creator

• Anderson, Joshua Wolff

Contributors dc:contributor

• Erik Linstead, Ph.D.
• Cyril Rakovski, Ph.D.
• Elizabeth Stevens, Ph.D.

• causal inference
• R
• software
• statistics
• mathematics
• programming
• Data Science

Repository record dc:identifier

https://digitalcommons.chapman.edu/cads_theses/13

OAI identifier oai:identifier

oai:digitalcommons.chapman.edu:cads_theses-1012