Abstract
Abstract In Instrumental Variables (IV) estimation, the effect of an instrument on an endogenous variable may vary across the sample. In this case, IV produces a local average treatment effect (LATE), and if monotonicity does not hold, then no effect of interest is identified. In this paper, I calculate the weighted average of treatment effects that is identified under general first-stage effect heterogeneity, which is generally not the average treatment effect among those affected by the instrument. I then describe a simple set of data-driven approaches to modeling variation in the effect of the instrument. These approaches identify a Super-Local Average Treatment Effect (SLATE) that weights treatment effects by the corresponding instrument effect more heavily than LATE. Even when first-stage heterogeneity is poorly modeled, these approaches considerably reduce the impact of small-sample bias compared to standard IV and unbiased weak-instrument IV methods, and can also make results more robust to violations of monotonicity. In application to a published study with a strong instrument, the preferred approach reduces error by about 19% in small (N ≈ 1, 000) subsamples, and by about 13% in larger (N ≈ 33, 000) subsamples.
Highlights
In order for instrumental variables (IV) estimation to identify a causal effect of interest, there are both theoretical and statistical conditions that must hold
I calculate the weighted average of treatment effects that is identified under general first-stage effect heterogeneity, which is generally not the average treatment effect among those affected by the instrument
Instrumental variables (IV) is at an odd point in its history. It seems that economists in general have grown more skeptical about instrument validity assumptions, or at least have shifted to higher standards for instruments
Summary
In order for instrumental variables (IV) estimation to identify a causal effect of interest, there are both theoretical (validity) and statistical (relevance) conditions that must hold. I provide software packages to aid in the usage of these methods.2 These new methods (1) identify a Super-Local Average Treatment Effect (SLATE), which is a weighted average of individual treatment effects, where weights are more strongly related to the impact of the instrument than in the LATE, (2) generally reduce noise in the IV bias term, improving statistical performance, (3) weaken the set of assumptions necessary for identification by relying on a weaker version of the monotonicity assumption in the group-interaction version of the estimator, and (4) give the researcher control over a tradeoff between bias and “localness” in the weighted version of the estimator. Huntington-Klein subsamples, combining my estimators with causal forest reduces mean absolute error by about 19% in small (N ≈ 1, 000) subsamples, and by about 13% in larger (N ≈ 33, 000) subsamples
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
