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When you say "control", I suspect you mean that you have a primary variable of interest, and then you have other variables that are potential confounders.

In the presence of a confounder, the effect size of the primary variable may appear higher or lower than it actually is (Simpson's Paradoxon / omitted variable bias). To "control" for this effect (see also here), the confounder must be added to the multiple regression (otherwise you lose the ability to infer the causal effect of the primary variable). In line with what @MindtheData discusses, see some more comments here on how understand causal structure in a regression setting, and which variables should and should not be added (e.g. colliders should not be added).

I disagree with the recommendation of @boulder — the causal structure determines which variables should go into the regression, regardless of significance or how they affect the estimates of other variables. Omission of confounders can lead to massive errors in inference. Other effects such as suppressors are secondary, and seldom crucial for the scientific conclusions.

When you say "control", I suspect you mean that you have a primary variable of interest, and then you have other variables that are potential confounders.

In the presence of a confounder, the effect size of the primary variable may appear higher or lower than it actually is (Simpson's Paradoxon / omitted variable bias). To "control" for this effect (see also here), the confounder must be added to the multiple regression (otherwise you lose the ability to infer the causal effect of the primary variable).

Note, however, that not all variables should be added to a regression. In some cases, adding a variable can even produce bias (collider). The causal structure determines which variables should go into the regression, regardless of significance or how they affect the estimates of other variables. See more comments here and in the excellent paper by Lederer et al., 2019.

When you say "control", I suspect you mean that you have a primary variable of interest, and then you have other variables that are potential confounders.

In the presence of a confounder, the effect size of the primary variable may appear higher or lower than it actually is (Simpson's Paradoxon / omitted variable bias). To "control" for this effect, the confounder must be added to the multiple regression (otherwise you lose the ability to infer the causal effect of the primary variable). In line with what @MindtheData discusses, see some more comments here on how understand causal structure in a regression setting, and which variables should and should not be added (e.g. colliders should not be added).

I disagree with the recommendation of @boulder — the causal structure determines which variables should go into the regression, regardless of significance or how they affect the estimates of other variables. Omission of confounders can lead to massive errors in inference. Other effects such as suppressors are secondary, and seldom crucial for the scientific conclusions.

When you say "control", I suspect you mean that you have a primary variable of interest, and then you have other variables that are potential confounders.

In the presence of a confounder, the effect size of the primary variable may appear higher or lower than it actually is (Simpson's Paradoxon / omitted variable bias). To "control" for this effect (see also here), the confounder must be added to the multiple regression (otherwise you lose the ability to infer the causal effect of the primary variable). In line with what @MindtheData discusses, see some more comments here on how understand causal structure in a regression setting, and which variables should and should not be added (e.g. colliders should not be added).

I disagree with the recommendation of @boulder — the causal structure determines which variables should go into the regression, regardless of significance or how they affect the estimates of other variables. Omission of confounders can lead to massive errors in inference. Other effects such as suppressors are secondary, and seldom crucial for the scientific conclusions.

When you say "control", I suspect that you mean that you have a primary variable of interest, and then you have other variables that are potential confounders.

In the presence of a confounder, the effect size of the primary variable may appear higher or lower than it actually is. To "control" for this effect, the confounder must be added to the multiple regression (otherwise you lose the ability to infer the causal effect of the primary variable). See some more comments here.

I therefore disagree with the recommendation of @boulder—the most important point is to see first if you have confounders, and if so, they should go in, regardless of significance or how they affect other variables. Omission of confounders can lead to massive errors in inference. Other effects such as suppressors are secondary, and seldom crucial for the scientific conclusions.

When you say "control", I suspect you mean that you have a primary variable of interest, and then you have other variables that are potential confounders.

In the presence of a confounder, the effect size of the primary variable may appear higher or lower than it actually is (Simpson's Paradoxon / omitted variable bias). To "control" for this effect, the confounder must be added to the multiple regression (otherwise you lose the ability to infer the causal effect of the primary variable). In line with what @MindtheData discusses, see some more comments here on how understand causal structure in a regression setting, and which variables should and should not be added (e.g. colliders should not be added).

I disagree with the recommendation of @boulder — the causal structure determines which variables should go into the regression, regardless of significance or how they affect the estimates of other variables. Omission of confounders can lead to massive errors in inference. Other effects such as suppressors are secondary, and seldom crucial for the scientific conclusions.