Suppose researchers were to randomly sample individuals who were already receiving invasive or non-invasive treatment of heart procedures, and found that those receiving non-invasive treatment lived longer on average than those receiving invasive treatment

Omitted variable bias (OVB) happens when a factual model leaves out at least one pertinent variables. The bias outcomes in the model crediting the impact of the missing variables to those that were incorporated.

All the more explicitly, OVB is the bias that shows up in the appraisals of boundaries in a regression analysis, when the accepted particular is off base in that it discards an autonomous variable that is a determinant of the reliant variable and connected with at least one of the included free variables.

these omitted variables prevents us from interpreting the results as causal because they are merely independent variable of the determinant of given dependent varibale thus it donot interefere in the results as the dependent variable does.

For example, Here in given example we dont know what the condition of individual is but given that heart procedure with invasive or non invasive treatment is done and main dependent variable hee is heart procedure and the treatment given not the doctor who is giving the procedures and the surrounding support which are independent on it own.