  We consider situations where a user feeds her attributes to a machine
learning method that tries to predict her best option based on a random sample
of other users. The predictor is incentive-compatible if the user has no
incentive to misreport her covariates. Focusing on the popular Lasso estimation
technique, we borrow tools from high-dimensional statistics to characterize
sufficient conditions that ensure that Lasso is incentive compatible in large
samples. We extend our results to the Conservative Lasso estimator and provide
new moment bounds for this generalized weighted version of Lasso. Our results
show that incentive compatibility is achieved if the tuning parameter is kept
above some threshold. We present simulations that illustrate how this can be
done in practice.
