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Causal inference for multiple treatments using fractional factorial designs
Author(s) -
Pashley Nicole E.,
Bind MarieAbèle C.
Publication year - 2023
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.11734
Subject(s) - fractional factorial design , factorial experiment , causal inference , observational study , factorial , plackett–burman design , inference , statistics , design of experiments , computer science , regression , main effect , mathematics , research design , econometrics , artificial intelligence , response surface methodology , mathematical analysis
Abstract We consider the design and analysis of multi‐factor experiments using fractional factorial and incomplete designs within the potential outcome framework. These designs are particularly useful when limited resources make running a full factorial design infeasible. We connect our design‐based methods to standard regression methods. We further motivate the usefulness of these designs in multi‐factor observational studies, where certain treatment combinations may be so rare that there are no measured outcomes in the observed data corresponding to them. Therefore, conceptualizing a hypothetical fractional factorial experiment instead of a full factorial experiment allows for appropriate analysis in those settings. We illustrate our approach using biomedical data from the 2003–2004 cycle of the National Health and Nutrition Examination Survey to examine the effects of four common pesticides on body mass index.