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Estimation under omitted confounders, endogeneity, omitted variable bias, and related problems

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Initial thoughts Estimating causal relationships from data is one of the fundamental endeavors of researchers, but causality is elusive. In the presence of omitted confounders, endogeneity, omitted variables, or a misspecified model, estimates of predicted values and effects of interest are inconsistent; causality is obscured. A controlled experiment to estimate causal relations is an alternative. […]

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