Don’t Put Too Much Meaning Into Control Variables

Update: The success of this blog post motivated us to formulate our point in a bit more detail in this paper, which is available on arXiv. Check it out if you need a citable version of the argument below.

I’m currently reading this great paper by Carlos Cinelli and Chad Hazlett: “Making Sense of Sensitivity: Extending Omitted Variable Bias”. They develop a full suite of sensitivity analysis tools for the omitted variable problem in linear regression, which everyone interested in causal inference should have a look at. While kind of a side topic, they make an important point on page 6 (footnote 6): Continue reading Don’t Put Too Much Meaning Into Control Variables

Econometrics: When Everybody is Different

Nowadays everybody is talking about heterogeneous treatment effects. That is, response to an economic stimulus that varies across individuals in a population. However, so far the discussion was concentrated on the instrumental variable setting where a randomized (natural or administered) experiment affects the treatment status of a so-called complier population. An average of the individual treatment effects can only be estimated for this group of compliers. Instead, for the always and never-takers we cannot say anything. But if individual treatment responses are different for everybody in the population, how can we be sure that what we’re estimating for the compliers is representative for the whole population? Continue reading Econometrics: When Everybody is Different