Double Robustness for Complier Parameters and a Semiparametric Test for Complier Characteristics
This provides a robustness check for researchers using instrumental variables in causal inference, though it is incremental as it builds on existing methods like Abadie (2003)'s framework.
The paper tackles the problem of assessing the external validity of instrumental variables by proposing a semiparametric test to check if complier subpopulations have consistent observable characteristics, applying it to reinterpret LATE estimates from Angrist and Evans (1998).
We propose a semiparametric test to evaluate (i) whether different instruments induce subpopulations of compliers with the same observable characteristics on average, and (ii) whether compliers have observable characteristics that are the same as the full population on average. The test is a flexible robustness check for the external validity of instruments. We use it to reinterpret the difference in LATE estimates that Angrist and Evans (1998) obtain when using different instrumental variables. To justify the test, we characterize the doubly robust moment for Abadie (2003)'s class of complier parameters, and we analyze a machine learning update to $κ$ weighting.