Omitted Variable Bias And What Can We Do About It

Omitted Variable Bias And What We Can Do About It

A step-by-step guide to understanding and acting upon the most pervasive type of bias

In causal inference, bias is extremely problematic because it makes inference not valid. Bias generally means that an estimator will not deliver the estimate of the true effect, on average.

This is why, in general, we prefer estimators that are unbiased, at the cost of a higher variance, i.e. more noise. Does …

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