Disentangling misreporting from genuine adaptation in strategic settings: a causal approach

In settings where ML models are used to inform the allocation of resources, agents affected by the allocation decisions might have an incentive to strategically change their features to secure better outcomes. While prior work has studied strategic responses broadly, disentangling misreporting from genuine adaptation remains a fundamental challenge. In this paper, we propose a causally-motivated approach to identify and quantify how much an agent misreports on average by distinguishing deceptive changes in their features from genuine adaptation. Our key insight is that, unlike genuine adaptation, misreported features do not causally affect downstream variables (i.e., causal descendants). We exploit this asymmetry by comparing the causal effect of misreported features on their causal descendants as derived from manipulated datasets against those from unmanipulated datasets. We formally prove identifiability of the misreporting rate and characterize the variance of our estimator. We empirically validate our theoretical results using a semi-synthetic and real Medicare dataset with misreported data, demonstrating that our approach can be employed to identify misreporting in real-world scenarios.

Definition 1 (Misreporting Rate). MR = P a (X * = 0|X = 1) This definition of the MR quantifies the conditional probability that a reported feature is false. We also show in Appendix C that our analysis can be trivially extended to other variants such as the false positive rate, P a (X = 1|X * = 0), and the marginal difference P a (X = 1) -P a (X * = 1).

Key to our suggested approach will be our ability to estimate the causal effect of X * on Y . We also require the typical assumptions used in causal inference, defined below. Assumption 3. The features X * , C, and the potential outcomes Y (X * = 1), Y (X * = 0) satisfy the following properties: