The Confidence Interval Method for Selecting Valid Instrumental Variables

Frank Windmeijer*, Xiaoran Liang, Fernando P Hartwig, Jack Bowden

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review


We propose a new method, the confidence interval (CI) method,
to select valid instruments from a larger set of potential instruments
for instrumental variables (IV) estimation of the causal effect of
an exposure on an outcome. Invalid instruments are such that they
fail the exclusion conditions and enter the model as explanatory variables.
The CI method is based on the confidence intervals of the per instrument
causal effects estimates and selects the largest group with all confidence
intervals overlapping with each other as the set of valid instruments.
Under a plurality rule, we show that the resulting standard IV, or
two-stage least squares (2SLS) estimator has oracle properties. This
result is the same as for the hard thresholding with voting (HT) method
of Guo et al. (2018). Unlike the HT method, the number of
instruments selected as valid by the CI method is guaranteed to be
monotonically decreasing for decreasing values of the tuning parameter.
For the CI method, we can therefore use a downward testing procedure
based on the Sargan (1958) test for overidentifying restrictions
and a main advantage of the CI downward testing method is that it
selects the model with the largest number of instruments selected
as valid that passes the Sargan test.
Original languageEnglish
Number of pages50
JournalJournal of the Royal Statistical Society: Series B
Publication statusAccepted/In press - 13 Jan 2021

Fingerprint Dive into the research topics of 'The Confidence Interval Method for Selecting Valid Instrumental Variables'. Together they form a unique fingerprint.

Cite this