Working Paper: CEPR ID: DP12804
Authors: Kfir Eliaz; Ran Spiegler
Abstract: We study a model in which a "statistician" takes an action on behalf of an agent, based on a random sample involving other people. The statistician follows a penalized regression procedure: the action that he takes is the dependent variable's estimated value given the agent's disclosed personal characteristics. We ask the following question: Is truth-telling an optimal disclosure strategy for the agent, given the statistician's procedure? We discuss possible implications of our exercise for the growing reliance on "machine learning" methods that involve explicit variable selection.
Keywords: No keywords provided
JEL Codes: No JEL codes provided
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Agent's truthful reporting of personal characteristics (D82) | OLS estimator is incentive compatible (C51) |
| Variable selection introduces incentive problems (C52) | Agent misreports characteristics (L85) |
| Asymmetric sample noise (C29) | Incentive compatibility failures (D82) |
| Exclusion of variables (C29) | Agent prefers to misreport characteristics (D82) |
| Variable selection aspect of penalized regression (C52) | Influences incentive structure for agents (D47) |