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0causal inference; semi-parametric models; environmental exposure; limit of detection; population intervention model
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Full-Text Articles in Medicine and Health Sciences
The Impact Of Coarsening The Explanatory Variable Of Interest In Making Causal Inferences: Implicit Assumptions Behind Dichotomizing Variables, Ori M. Stitelman, Alan E. Hubbard, Nicholas P. Jewell
The Impact Of Coarsening The Explanatory Variable Of Interest In Making Causal Inferences: Implicit Assumptions Behind Dichotomizing Variables, Ori M. Stitelman, Alan E. Hubbard, Nicholas P. Jewell
U.C. Berkeley Division of Biostatistics Working Paper Series
It is common in analyses designed to estimate the causal effect of a continuous exposure/treatment to dichotomize the variable of interest. By dichotomizing the variable and assessing the causal effect of the newly fabricated variable practitioners are implicitly making assumptions. However, in most analyses these assumptions are ignored. In this article we formally address what assumptions are made in dichotomizing variables to assess causal effects. We introduce two assumptions, either of which must be met, in order for the estimates of the causal effects to be unbiased estimates of the parameters of interest. We title those assumptions the Mechanism Equivalence …