Tehran University of Medical Sciences Journal of Biostatistics and Epidemiology 2383-4196 1 3/4 2015 10 19 121 128 EN Kazem Mohammad Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran Seyed Saeed Hashemi-Nazari Safety Promotion and Injury Prevention Research Center AND Department of Epidemiology, School of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran Nasrin Mansournia Department of Endocrinology, School of Medicine, AJA University of Medical Sciences, Tehran, Iran MohammadAli Mansournia Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran 2015 10 13 2015 10 13 Conditional  methods  of adjustment  are often used to quantify  the effect  of the exposure on the outcome.  As  a  result,  the  stratums-specific  risk  ratio  estimates  are  reported  in  the  presence  of interaction   between   exposure  and  confounder(s)   in  the  literature,  even  if  the  target  of  the intervention on the exposure is the total population and the interaction itself is not of interest. The reason is that researchers and practitioners  are less familiar with marginal methods of adjustment such as inverse-probability-weighting  (IPW) and standardization and marginal causal effects which have causal interpretations for the total population even in the presence of interaction. We illustrate the relation  between  marginal  causal  effects  estimated  by IPW and standardization  methods  and conditional  causal effects estimated  by traditional  methods in four simple scenarios based on the presence  of  confounding   and/or  effect  modification.   The  data  analysts   should  consider   the intervention level of the exposure for causal effect estimation, especially in the presence of variables which are both confounders and effect modifiers. https://jbe.tums.ac.ir/index.php/jbe/article/view/8 https://jbe.tums.ac.ir/index.php/jbe/article/download/8/32