Mostly Harmless Econometrics: An Empiricist’s Companion

Heterogeneity and Nonlinearity

As we saw in the previous section, a linear causal model in combination with the CIA leads to a linear CEF with a causal interpretation. Assuming the CEF is linear, the population regression is it. In practice, however, the assumption of a linear CEF is not really necessary for a causal interpretation of regression. For one thing, as discussed in Section 3.1.2, we can think of the regression of Y; on X; and S; as providing the best linear approximation to the underlying CEF, regardless of its shape. Therefore, if the CEF is causal, the fact that regression approximates it gives regression coefficients a causal flavor. This claim is a little vague, however, and the nature of the link between regression and the CEF is worth exploring further. This exploration leads us to an understanding of regression as a computationally attractive matching estimator.

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Mostly Harmless Econometrics: An Empiricist’s Companion

Regression Details

1.4.1 Weighting Regression Few things are as confusing to applied researchers as the role of sample weights. Even now, 20 years post - Ph. D., we read the section of …

Getting a Little Jumpy: Regression Discontinuity Designs

But when you start exercising those rules, all sorts of processes start to happen and you start to find out all sorts of stuff about people... Its just a way …

Two-Sample IV and Split-Sample IVF

GLS estimates of Г in (4.3.1) are consistent because E The 2SLS minimand can be thought of as GLS applied to equation (4.3.1), after multiplying by /N to keep the …

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