TY  - JOUR
PY  - 2020//
TI  - Equivalence model: a new graphical model for causal inference
JO  - Epidemiology and health
A1  - Poorolajal, Jalal
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - OBJECTIVES: Several causal models have been proposed. One essential question that has still been remained unanswered is why some high-risk people for a particular disease do not get the disease while some low-risk people get it. The "equivalence model" explains this dilemma. <br><br>METHODS: The equivalence model describes graphically the overall effect of risk and protective factors at the individual level. Several risk factors facilitate the occurrence of the event (disease) whereas several protective factors mitigate or prevent the occurrence of the event. The equivalence model explains how the overall effect may or may not lead to the occurrence of the event. <br><br>RESULTS: The risk factors are denoted by 'R' and the protective factors by 'P'. When there is a balance between risk and protective factors. Neither of the factors can overcome another one, therefore, the outcome will not occur. The outcome will occur when the units of the risk factor are greater than the units of the protective factor. On the other hand, the outcome will not occur when the units of the risk factor are less than or equal to the units of the protective factor. <br><br>CONCLUSION: This model can simply describe the causal inference in the complicated situations with several known and known risk and protective factors and justify how a group of people with a low level of certain risk factor(s) may be affected by a certain disease while another group of people with a higher level of the same risk factor(s) may remain safe.<p />  <p>Language: en</p>
LA  - en
SN  - 2092-7193
UR  - http://dx.doi.org/10.4178/epih.e2020024
ID  - ref1
ER  -