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.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Anderson, Joshua Wolff"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Thesis","degree_discipline":"Computational and Data Sciences","degree_department":null,"school":null,"contributors":["Erik Linstead, Ph.D.","Cyril Rakovski, Ph.D.","Elizabeth Stevens, Ph.D."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-05-01T07:00:00Z","date_published":"2022-05-01T07:00:00Z","updated_at":"2026-07-24T01:38:24Z","subjects":["causal inference","R","software","statistics","mathematics","programming","Data Science"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.chapman.edu/cads_theses/13","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Erik Linstead, Ph.D.","Cyril Rakovski, Ph.D.","Elizabeth Stevens, Ph.D."]},{"key":"dc:creator","label":"Author","values":["Anderson, Joshua Wolff"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2024-04-28T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computational and Data Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["causal inference","R","software","statistics","mathematics","programming","Data Science"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.chapman.edu/cads_theses/13"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:source","label":"Dc Source","values":["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"]},{"key":"dc:title","label":"Title","values":["CausalModels: An R Library for Estimating Causal Effects"]}]}],"canonical_facts":{"dc:contributor":["Erik Linstead, Ph.D.","Cyril Rakovski, Ph.D.","Elizabeth Stevens, Ph.D."],"dc:creator":["Anderson, Joshua Wolff"],"dc:date.available":["2024-04-28T07:00:00Z"],"dc:description.abstract":["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.
"],"dc:identifier":["https://digitalcommons.chapman.edu/cads_theses/13"],"dc:source":["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"],"dc:subject":["causal inference","R","software","statistics","mathematics","programming","Data Science"],"dc:title":["CausalModels: An R Library for Estimating Causal Effects"],"thesis:degree_discipline":["Computational and Data Sciences"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T01:38:24Z"}