Abstract
In many predictive decision-making scenarios, such as credit scoring and
academic testing, a decision-maker must construct a model (predicting some
outcome) that accounts for agents' incentives to "game" their features in order
to receive better decisions. Whereas the strategic classification literature
generally assumes that agents' outcomes are not causally dependent on their
features (and thus strategic behavior is a form of lying), we join concurrent
work in modeling agents' outcomes as a function of their changeable attributes.
Our formulation is the first to incorporate a crucial phenomenon: when agents
act to change observable features, they may as a side effect perturb hidden
features that causally affect their true outcomes.
We consider three distinct desiderata for a decision-maker's model:
accurately predicting agents' post-gaming outcomes (accuracy), incentivizing
agents to improve these outcomes (improvement), and, in the linear setting,
estimating the visible coefficients of the true causal model (causal
precision). As our main contribution, we provide the first algorithms for
learning accuracy-optimizing, improvement-optimizing, and
causal-precision-optimizing linear regression models directly from data,
without prior knowledge of agents' possible actions. These algorithms
circumvent the hardness result of Miller et al. (2019) by allowing the decision
maker to observe agents' responses to a sequence of decision rules, in effect
inducing agents to perform causal interventions for free.
Description
[2002.10066] Learning From Strategic Agents: Accuracy, Improvement, and Causality
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