TY  - INPR
ID  - discovery10177886
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
SN  - 1368-4221
JF  - Econometrics Journal
AV  - restricted
N2  - We propose a semi-parametric test to evaluate (a) whether different instruments induce subpopulations of compliers with the same observable characteristics, on average; and (b) whether compliers have observable characteristics that are the same as the full population, treated subpopulation, or untreated subpopulation, on average. The test is a flexible robustness check for the external validity of instruments. To justify the test, we characterise the doubly robust moment for Abadie?s class of complier parameters, and we analyse a machine learning update to weighting that we call the automatic 
 weight. We use the test to reinterpret Angrist and Evans' different local average treatment effect estimates obtained using different instrumental variables.
Y1  - 2023/10/09/
PB  - Wiley-Blackwell
A1  - Sun, Liyang
A1  - Singh, Rahul
UR  - https://doi.org/10.1093/ectj/utad019
KW  - Semiparametric and Nonparametric Methods
KW  -  Instrumental Variables (IV) Estimation
KW  -  Neural Networks and Related Topics
TI  - Double robustness for complier parameters and a semi-parametric test for complier characteristics
ER  -