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Does using stepwise variable selection to build sequential path analysis models make sense?
Author(s) -
Kozak Marcin,
Azevedo Ricardo A.
Publication year - 2011
Publication title -
physiologia plantarum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.351
H-Index - 146
eISSN - 1399-3054
pISSN - 0031-9317
DOI - 10.1111/j.1399-3054.2010.01431.x
Subject(s) - causal inference , path analysis (statistics) , inference , selection (genetic algorithm) , causal model , variable (mathematics) , path (computing) , causal analysis , computer science , causal structure , structural equation modeling , machine learning , artificial intelligence , econometrics , mathematics , statistics , mathematical analysis , physics , quantum mechanics , programming language
Causal inference methods – mainly path analysis and structural equation modeling – offer plant physiologists information about cause‐and‐effect relationships among plant traits. Recently, an unusual approach to causal inference through stepwise variable selection has been proposed and used in various works on plant physiology. The approach should not be considered correct from a biological point of view. Here, it is explained why stepwise variable selection should not be used for causal inference, and shown what strange conclusions can be drawn based upon the former analysis when one aims to interpret cause‐and‐effect relationships among plant traits.