Title: Disentangling misreporting from genuine adaptation in strategic settings: a causal approach
Venue: NeurIPS 2025
Accepted: yes
Keywords: Causal Inference

Machine Learning

Strategic Adaptation

Misreporting
Abstract: 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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the challenge of distinguishing between genuine modification and misreporting of features by agents in settings where machine learning models are used for resource allocation, such as in loan applications or government insurance payouts. Agents may either genuinely change their behavior (genuine modification) or simply report false features (misreporting) to obtain better outcomes. The authors propose a causally-motivated approach to identify and quantify the average rate of misreporting by leveraging the fact that misreported features, unlike genuinely modified ones, do not causally affect downstream variables. By comparing the causal effect of features on their descendants in both manipulated (potentially misreported) and unmanipulated datasets, they develop an estimator for the misreporting rate. The paper provides theoretical guarantees for the identifiability and variance of their estimator, and demonstrates its effectiveness with experiments on both semi-synthetic and real Medicare datasets.\n\nSTRENGTHS AND WEAKNESSES:\n1. Novel Causal Framework: The paper introduces a new causal approach for disentangling genuine modification from misreporting, providing a theoretically grounded solution to a longstanding problem in strategic behavior and resource allocation settings.\n\n2. Theoretical Guarantees: The authors rigorously prove the identifiability of the misreporting rate and analyze the variance of their estimator, offering strong theoretical justification and transparency for their method.\n\n3. Empirical Validation: The approach is thoroughly validated on both semi-synthtic and real-world datasets, demonstrating its practical utility and superior performance.\n\n\n*Weakness*\n\n1. Strong Assumptions Needed: The method relies on some pretty strong assumptions, like no unobserved confounding, which might not always hold in real-world data. If those assumptions are violated, the results could be off.\n\n2. Requires Special Datasets: You need access to both manipulated (possibly misreported) and unmanipulated datasets. In practice, getting a “clean” dataset where no one has any incentive to misreport isn’t always realistic.\n\n3. Limited Scope for Complex Behavior: The framework mostly handles binary features and assumes misreporting only happens in one direction. It doesn’t really cover more complex or nuanced types of misreporting, so its scope is a bit limited.\n\nQUESTIONS:\nSee Weakness.\n\nLIMITATIONS:\nNA", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work proposes a causal identification and estimation framework for characterizing settings in which agents may strategically manipulate their characteristics to obtain improved outputs. In particular, the authors disentangle to forms of agent responses: genuine modification, in which an agent alters characteristics in such a way that changes their downstream outcomes, and misreporting, in which agents report incorrect features without a change in their underlying features. The paper establishes the identifying conditions under which the misreporting rate can be identified via a corpus with no misreporting and genuine modification + misreporting. The authors propose an estimator for this estimated and analyze its variance properties, then validate it via synthetic and semi-synthetic experiments.\n\nSTRENGTHS AND WEAKNESSES:\n## Significance.\n\nThe problem of strategic response to incentives is timely and important. The authors clearly articulate the relevance of this problem via a well-motivated example involving the U.S. Medicare Advantage (MA) program, and show that the proposed approach can be used to address policy-relevant estimation problems in this setting. This is a strong example of use case inspired basic research, that likely generalizes to other similar settings. \n\n## Originality. \n\nThe authors clearly situate the proposed work in the context of other related literature in this space and articulate the technical novelty. \n\nMinor: line 74 claims that prior work by Chang et al. [5] can only partially identify misreporting rates - this is not a limitation of their approach, per se, if this existing framework depends on weaker assumptions. Please clarify this in the next version of the draft. \n\n\n## Clarity.\n\nThe work is generally clear and easy to follow. The use of a running example throughout the work is effective in motivating the technical components. \n\nMinor: \n- The use of observed and true features in potential outcome notation $X(X^*=x^*)$ (line 119) is slightly awkward. I do not see this frequently used throughout the text. Consider revising this notation if possible. \n- In Section 4.1, the build-up to Theorem 1 is somewhat long. Consider shifting Theorem 1 towards the beginning of the section to communicate the key idea then draw connections to Lemma 1 after for intuition. If I am following correctly, Thm 1 does not inherit a direct dependence on Lemma 1 and Lemma 1 is included for pedagogical purposes. \n- Line 206: extra equation number. \n- Lines 144-152. Consider translating this text description of the limitations of status quo estimation approaches into a compelling main figure to clearly convey why the proposed approach is important over a naive baseline. This figure might include an illustrative empirical result from the appendix. \n\n## Quality.\n\nThis work generally meets standards for a causal inference paper with the stated objectives. Assumptions are, for the most part, clearly stated. The identification analysis is clean and interesting. The estimation approach and corresponding experiments are generally well-motivated. That said, I do have several key concerns that affect my scores: \n\n### Restrictive modeling setup. \n\nA key concern I have with the current setup is that the misreporting rate is assumed to only depend on the true feature values $X^\\star$. This assumes that no features confound the relationship between $X^\\star$ and $X$ - which is generally quite strong given the motivating example provided by the authors (e.g., misreporting could be higher for patients with certain medical conditions or coverage). Complicating maters further, this assumption seems to be a core technical requirement of the framework. Eg., the authors assume the misreported group will be a random sample of the group where $X^\\star=0$ (line 172). \n\nAt minimum, this should be clearly acknowledged and explained in the preliminaries and limitations. Ideally, there should be further theoretical/experimental analysis probing robustness to this assumptions' violations. For instance, a few guiding questions are: does the existing analysis carry through if we condition on a confounding variable  $X<-V->X^*$ (where $V$ is the confounder) via a stratification approach? If we took such an approach, how would this affect variance of the estimator in cases where some strata have small treatment effects? Is there a way to test whether this independence assumption is violated in practice?\n\nThe assumption that $X$ and $X^\\star$ are dichotomous is also restrictive, albeit less concerning than the assumption above. \n\nOn the other hand, the generality of the framework across settings Fig 1 (a-c) is a clear strength. \n\n### Variance of the estimator.\n\nAs noted in Section 4.2, the proposed estimator will have high variance in cases where the true treatment effect in the unmanipulated dataset is small. We observe this empirically in Figure 2 (center), where confidence intervals are wide when the effect of $X^\\star$ on Y is small. While some variance in the estimator is tolerable, I have concerns that the extent of the variance in these results may affect the substantive conclusions that can be drawn from the estimation approach. For instance, when $\\beta_{X^\\star}=0.1$, the interval spans roughly 0.03-0.4, which is quite large. To what extent are values of the $\\beta_{X^\\star}$ range reported in Figure 2 reasonable? More concretely, what is the estimated $\\beta_{X^\\star}$ value in the experiments with real data, as a reference point? \n\nFurther, we also observe wide confidence intervals with real data experiments. While the authors note that their approach is the only one that provides MR rates not statistically significantly different from zero (line 342), this is trivially satisfied by wide confidence intervals. For instance, the upper and lower bounds exceed those provided by NDEE and NMRE for Fig. 3 (center).\n\nI have further concerns that the experimental setup may mask further variance issues that would arise under realistic application conditions. In particular, the authors report exclusion conditions (line 318-320) designed to reduce variance in estimates. What percent of records were omitted on the basis of these criteria? How do the results look with these filters omitted? \n\n### Estimators & Experiment Setup. \n\nThe below are more minor points. \n\nFirst, the authors leverage S-learners for estimation. While this is a reasonable starting point, this approach is likely to have larger variance than other DR approaches that apply a first-order bias correction. Expanding to DR estimators may improve the variance properties of the proposed estimator. \n\nSecond, the synthetic experiment with hard-coded coefficients may affect the results. Because this is (understandably) the only experiment with access to true misreporting rates, the setup here is important. The authors should report robustness experiments examining how results change under different D.G.P setups (or with randomly sampled coefficients).\n\nQUESTIONS:\nIn general I think this work makes a valuable contribution, but have concerns in light of my review above. I will raise my score if the questions below are addressed in a satisfactory manner. \n\n- How robust is the theoretical and experimental results to the the misreporting rate is assumed to only depend on the true feature values $X^{*}$? How easily can the framework be extended to support subgroup-dependent misreporting rates? \n\n- To what extent are values of the $\\beta$ range reported in Figure 2 reasonable? More concretely, what is the estimated $\\beta$ value in the experiments with real data, as a reference point?\n\n\n- How robust are the real-data experimental findings to the exclusion conditions designed to reduce variance, and why are these exclusion conditions reasonable? How robust are the synthetic results to coefficient values?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the problem of detecting and quantifying misreporting by strategic agents in settings where machine learning models are used for resource allocation decisions. The key challenge tackled is distinguishing between two types of strategic behavior: genuine modification and misreporting. The authors' core insight is that misreported features do not causally affect downstream variables, unlike genuinely modified features. Leveraging this asymmetry, they compare causal effects derived from manipulated datasets (where misreporting may occur) and unmanipulated datasets (where misreporting is absent) to quantify the misreporting rate.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n- The paper provides formal definitions, assumptions, and proofs for identifiability (Lemma 1, Theorem 1) with a clear mathematical treatment.\n- Addresses a gap in the literature by formally distinguishing misreporting from genuine behavioral change.\n- Using the asymmetric effect on causal descendants to identify misreporting is innovative.\n\nWeaknesses\n- The paper could better explain why misreporting doesn't affect causal descendants before diving into technical details.\n- Identifies average misreporting rates but not individual ones.\n- The no unmeasured confounding assumption (Assumption 3) is untestable and potentially unrealistic in many real-world settings.\n- The reliance on causal descendants means the method may not apply to settings where downstream effects are unobservable or delayed.\n- The approach is most useful in settings where agents have incentives to misreport and unmanipulated data is available, which may not cover all strategic ML scenarios.\n\nQUESTIONS:\n- How robust is the method to violations of the \"no unobserved confounding\" assumption (Assumption 3)?\n- Can the method be generalized to continuous or categorical features?\n- What are the barriers to deploying CMRE in practice (e.g., data access, model transparency)?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}