Sign Hacking with Auxiliary Variable Exploration in the Age of Big Data

In linear regression, the signs of coefficients convey the direction of covariate effects and are central to empirical interpretation.

In high-dimensional settings, however, the abundance of candidate covariates introduces substantial model selection uncertainty.

We study the deliberate manipulation of coefficient signs through the inclusion of a carefully chosen auxiliary variable, a practice we term SHAVE (\textit{Sign Hacking with Auxiliary Variable Exploration}).

We show that, conditional on the outcome and variables of interest, there exists a set of auxiliary-variable realizations with positive Lebesgue measure that lead to sign reversals upon inclusion.

Moreover, with high probability, such variables can be found when many auxiliary candidates are available, leading simultaneously to reversed signs, inflated $t$- and $F$-statistics.

Simulation studies and an empirical application corroborate these theoretical findings.

We further propose detection strategies for SHAVE when augmented or independent datasets are available, as SHAVE has important implications for reproducibility, $p$-hacking, and research integrity.