# Omitted variable bias vs. Multicollinearity

There's seems to be a bit like catch 22: suppose I am doing linear regression, and I have 2 variables that are highly correlated. If I use both in my model, I will suffer from multicollinearity, but if I don't put both I will suffer from omitted variable bias?

• Usually, you would not care about both of them simultaneously. Depending on the goal of your analysis (say, description vs. prediction), you would only care about one of them. For description, multicollinearity is just a fact to be mentioned, just one of the characteristics of the data. For prediction, omitted variable bias is largely irrelevant. – Richard Hardy Mar 14 '20 at 19:13
• What do you think about my answer? Does it answer your question? If so, you may accept it by clicking on the tick mark to the left. Otherwise, you may ask for further clarification. This is how Cross Validated works. – Richard Hardy May 2 '20 at 6:29
• Even though it makes sense, I need to give it some deeper thought. So I won't be accepting it yet. – Maverick Meerkat May 2 '20 at 9:32
• Sure. I have also given it more though and have appended my answer. – Richard Hardy May 2 '20 at 10:39
• Richard is right in the sense that your goal matters but it seems there is confusion about "prediction". Predictions and model performance are generally considered unaffected by multicollinearity. Multicollinearity is a bigger concern when you want to describe the relationships in sample estimated by the beta coefficients or make inferences on the true values/relationships of the betas. – LSC May 2 '20 at 10:46

Usually, you would not care about both of them simultaneously. Depending on the goal of your analysis (say, description vs. prediction vs. causal inference), you would care about at most one of them.

Description$$\color{red}{^*}$$
Multicollinearity (MC) is just a fact to be mentioned, just one of the characteristics of the data to report.
The notion of omitted variable bias (OVB) does not apply to descriptive modelling. (See the definition of OVB in the Wikipedia quote provided below.) In contrast to causal modelling, the causal notion of relevance of variables does not apply for description. You can freely choose the variables you are interested in describing probabilistically (e.g. in the form of a regression) and you evaluate your model w.r.t. the chosen set of variables, not variables not chosen.

Prediction
MC and OVB are largely irrelevant as you are not interested in model coefficients per se, only in predictions.

Causal modelling / causal inference
You may care about both MC and OVB at once when attempting to do causal inference. I will argue that you should actually worry about the OVB but not MC. OVB results from a faulty model, not from the characteristics of the underlying phenomenon. You can remedy it by changing the model. Meanwhile, imperfect MC can very well arise in a well specified model as a characteristic of the underlying phenomenon. Given the well specified model and the data that you have, there is no sound escape from MC. In that sense you should just acknowledge it and the resulting uncertainty in your parameter estimates and inference.

$$\color{red}{^*}$$I am not 100% sure about the definition of description / descriptive modelling. In this answer, I take description to constitute probabilistic modelling of data, e.g. joint, conditional and marginal distributions and their specific features. In contrast to causal modelling, description focuses on probabilistic but not causal relationships between variables.

Edit to respond to feedback by @LSC:

In defence of my statement that OVB is largely irrelevant for prediction, let us first see what OVB is. According to Wikipedia,

In statistics, omitted-variable bias (OVB) occurs when a statistical model leaves out one or more relevant variables. The bias results in the model attributing the effect of the missing variables to the estimated effects of the included variables. More specifically, OVB is the bias that appears in the estimates of parameters in a regression analysis, when the assumed specification is incorrect in that it omits an independent variable that is a determinant of the dependent variable and correlated with one or more of the included independent variables.

In prediction, we do not care about the estimated effects but rather accurate predictions. Hence, my statement above should become obvious.

Regarding the statement OVB will necessarily introduce bias into the estimation process and can screw with predictions by @LSC.

• This is tangential to my points because I did not discuss the effect of omitting a variable on prediction. I only discussed the relevance of omitted variable bias for prediction. The two are not the same.
• I agree that omitting a variable does affect prediction under imperfect MC. While this would not be called OVB (see the Wikipedia quote above for what OVB typically means), this is a real issue. The question is, how important is that under MC? I will argue, not so much.
• Under MC, the information set of all the regressors vs. the reduced set without one regressor are close. As a consequence, the loss of predictive accuracy from omitting a regressor is small, and the loss shrinks with the degree of MC. This should come as no surprise. We are routinely omitting regressors in predictive models so as to exploit the bias-variance trade-off.
• Also, the linear prediction is unbiased w.r.t. the reduced information set, and as I mentioned above, that information set is close to the full information set under MC. The coefficient estimators are also predictively consistent; see "T-consistency vs P-consistency" for a related point.
• "Prediction" as used by 99.9999% of statisticians would be a case where multicollinearity is generally irrelevant. "Inference" or even just describing the relationships estimated with beta coefficients is when multicollinearity matters more. Multicollinearity does not cause bias in the estimation process and therefore, prediction (predicted Y values or model performance) is almost always considered unaffected by multicollinearity. Omitting pertinent variables, though, causes misspecification and can introduce at least bias but possibly inconsistency into the estimation procedure. – LSC May 2 '20 at 10:43
• @LSC, multicollinearity does not cause bias, but this applies equally to prediction and inference. Importantly, while multicollinearity causes high variance, some combinations of parameters have low variance, e.g. the linear combination that is used for prediction in linear regression. That is why multicollinearity does not matter much for prediction. Meanwhile, the high variance of individual parameters is a problem in inference, as the high uncertainty of the point estimates is undesirable. – Richard Hardy May 2 '20 at 10:53
• @LSC, (continued) Meanwhile, omitted variables cause not only finite-sample, but also asymptotic bias (hence also inconsistency w.r.t. to the true parameter), except when the omitted variables are orthogonal to the space of the included regressors. – Richard Hardy May 2 '20 at 10:55
• @LSC, you mentioned confusion regarding prediction in a comment above and stressed the 99%... bit, but I do not see any confusion. While none of us has specified the notion of prediction explicitly, we seem to agree well on the implications of multicollinearity w.r.t. it. – Richard Hardy May 2 '20 at 11:55
• Why the downvote? I would appreciate some constructive feedback so that I can improve my answer. Thank you. – Richard Hardy May 2 '20 at 14:09

If your goal is inference, multicollinearity is problematic. Consider multiple linear regression where the beta parameters help us estimate the increase or decrease in Y for a unit increase in X1, all other variables held constant. Multicollinearity has the effect of inflating the standard errors of the beta parameters, making such inferences less reliable. Specifically, the variances of the model coefficients become very large so that small changes in the data can precipitate erratic changes in model parameters.

If the purpose of the regression model is to investigate associations, multicollinearity among the predictor variables can obscure the computation and identification of key independent effects of collinear predictor variables on the outcome variable because of the overlapping information they share.

(source)

However, multicollinearity does not prevent good, reliable predictions in the scope of the model.

In general, multicollinearity is acceptable when the goal is prediction, but if multicollinearity is present, it is something you should disclose and it affects the uncertainty surrounding your model estimates.

Be aware that perfect multicollinearity actually leads to a situation in which an infinite number of fitted regression models is possible. The VIF (variance inflation factor) is one rule-of-thumb for how much multicollinearity we can tolerate in inference.

In a model with perfect multicollinearity, your regression coefficients are indeterminate and their standard errors are infinite

(source).

• Welcome to Cross Validated! Unfortunately, I think several of your statements are incorrect. Impecfect multicollinearity does not invalidate any assumptions. The coefficients do not lose interpretability or meaning either. The only thing that happens is that confidence bounds get wide. – Richard Hardy May 2 '20 at 10:58
• @RichardHardy see my edit for sources. You are right that the confidence bounds get wide, and that is my meaning. The confidence bounds widen to infinity as the degree of multicollinearity increases, making coefficients unstable. Small changes in the data can cause coefficients to change erratically. – Timothy May 2 '20 at 11:12
• Multicollinearity (mc) can be perfect or imperfect. The original post specifically discusses imperfect mc: I have 2 variables that are highly correlated. Thus my points. Now it is a little unclear which type of mc you are discussing because your description does not fit either type but is a mix of both. Perfect mc (which is irrelevant to the OP) yields unidentified and in a sense meaningless point estimates and undefined/infinite (not just large) standard errors. I think you could improve your answer by making the distinction specific and explicitly stating the case you are discussing. – Richard Hardy May 2 '20 at 11:51
• @RichardHardy I agree I should clarify the distinction between perfect and imperfect mc. Thanks for your feedback. I will revise my answer accordingly. I wanted to bring light to this point from your comment: "the high variance of individual parameters is a problem in inference." I feel this is a key insight. But when you assert "For description, multicollinearity is just a fact to be mentioned" it might be helpful to clarify the thing about the variances, for those who are wondering why multicollinearity can be a bad thing. – Timothy May 2 '20 at 20:07