# Model Reconstruction Using Counterfactual Explanations: A Perspective From Polytope Theory Source: [Model Reconstruction Using Counterfactual Explanations: A Perspective From Polytope Theory]() Collection index: [llms.txt]() Data updated: 2025-10-25T17:43:27.549Z Authors: Pasan Dissanayake, Sanghamitra Dutta Published: 2024-05-08T18:52:47.000Z ## Abstract Counterfactual explanations provide ways of achieving a favorable model outcome with minimum input perturbation. However, counterfactual explanations can also be leveraged to reconstruct the model by strategically training a surrogate model to give similar predictions as the original (target) model. In this work, we analyze how model reconstruction using counterfactuals can be improved by further leveraging the fact that the counterfactuals also lie quite close to the decision boundary. Our main contribution is to derive novel theoretical relationships between the error in model reconstruction and the number of counterfactual queries required using polytope theory. Our theoretical analysis leads us to propose a strategy for model reconstruction that we call Counterfactual Clamping Attack (CCA) which trains a surrogate model using a unique loss function that treats counterfactuals differently than ordinary instances. Our approach also alleviates the related problem of decision boundary shift that arises in existing model reconstruction approaches when counterfactuals are treated as ordinary instances. Experimental results demonstrate that our strategy improves fidelity between the target and surrogate model predictions on several datasets. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Model Reconstruction on Adult Income](): Model Reconstruction; Adult Income; Average Fidelity: 94. - [Model Reconstruction on COMPAS](): Model Reconstruction; COMPAS; Avg Fidelity: 94. - [Model Reconstruction on HELOC](): Model Reconstruction; HELOC; Average Fidelity: 93. - [Model Reconstruction on Housing](): Model Reconstruction; Housing; Fidelity: 71.6. - [Model Reconstruction on DCCC](): Model Reconstruction; DCCC; Average Fidelity: 91. ## Original sources and implementation [Original paper]() [PDF]()