# Questions tagged [omitted-variable-bias]

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### How is the omitted variable bias formula derived?

I see it is often quoted that the omitted variable bias formula is $$\text{Bias}\left(\widehat{\beta_1}\right) = \beta_2 \cdot \text{Corr}\left(X_2,X_1\right)$$ where $\widehat{\beta_1}$ is the ...
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### Omitted variable problem

I'm studying the cases in which the endogeneity problem arises in OLS regression. Suppose we have the following population equation: $y=\beta_0 +\beta_1 x_1 + ... + \beta_k x_k + \gamma q + \epsilon$ ...
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### What would it take for the omitted-variable bias from multiple omitted variables to cancel out?

Let's stick to ordinary least squares linear regression for now, and assume the typical conditions for the Gauss-Markov theorem. If it is helpful to assume Gaussian errors, that's fine. In such a ...
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### Difference Omitted Variable Bias and Confounding?

Is there a difference between omitted variable bias and confounding bias in linear models? To my knowledge, when investigating the causal effect of $X$ on $Y$, a confounder is a variable $Z$ that is ...
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### Endogeneity coming from omitted variable vs measurement error

Can someone explain more clearly what is a measurement error and how is it different from omitted variable. I know the theoretical implications, but I don't really know how to tell which problem I'm ...
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### Omitted variable bias in ordered logistic regression query

Including too few variables in OLS regression means that the coefficient estimates can be biased, e.g. if we aren't controlling for a variable in a model that should be there, it is instead captured ...
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### Can a confounding factor hide a possible causal relationship? (as opposed to find a spurious one)

I'm a rookie with statistics, and I'm struggling to understand this: it is well known that a confounding factor can cause a spurious association, leading to rejecting a true null hypothesis (i.e. due ...
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### Does confounding always imply endogeneity?

I'm a bit confused with the definitions regarding causal inference. My question is whether we can call measured confounding an endogeneity problem?
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### Are coefficients that are zero omitted variable bias?

If a regression coefficient is essentially zero, doesn't that imply that there is (massive) omitted variable bias? That is, the change must then exist in the error term. The classic definition of OVB ...
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### 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 ...
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