Theoretical Evaluation of Asymmetric Shapley Values for Root-Cause Analysis

• URL: http://arxiv.org/abs/2310.09961v1
• Date: Sun, 15 Oct 2023 21:40:16 GMT
• Title: Theoretical Evaluation of Asymmetric Shapley Values for Root-Cause Analysis
• Authors: Domokos M. Kelen, Mih\'aly Petreczky, P\'eter Kersch, Andr\'as A. Bencz\'ur
• Abstract summary: Asymmetric Shapley Values (ASV) is a variant of the popular SHAP additive local explanation method. We show how local contributions correspond to global contributions of variance reduction. We identify generalized additive models (GAM) as a restricted class for which ASV exhibits desirable properties.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we examine Asymmetric Shapley Values (ASV), a variant of the popular SHAP additive local explanation method. ASV proposes a way to improve model explanations incorporating known causal relations between variables, and is also considered as a way to test for unfair discrimination in model predictions. Unexplored in previous literature, relaxing symmetry in Shapley values can have counter-intuitive consequences for model explanation. To better understand the method, we first show how local contributions correspond to global contributions of variance reduction. Using variance, we demonstrate multiple cases where ASV yields counter-intuitive attributions, arguably producing incorrect results for root-cause analysis. Second, we identify generalized additive models (GAM) as a restricted class for which ASV exhibits desirable properties. We support our arguments by proving multiple theoretical results about the method. Finally, we demonstrate the use of asymmetric attributions on multiple real-world datasets, comparing the results with and without restricted model families using gradient boosting and deep learning models.

Related papers

• Multiply-Robust Causal Change Attribution [15.501106533308798]
  We develop a new estimation strategy that combines regression and re-weighting methods to quantify the contribution of each causal mechanism. Our method demonstrates excellent performance in Monte Carlo simulations, and we show its usefulness in an empirical application.
  arXiv  Detail & Related papers  (2024-04-12T22:57:01Z)
• Approximation-Generalization Trade-offs under (Approximate) Group Equivariance [3.0458514384586395]
  Group equivariant neural networks have demonstrated impressive performance across various domains and applications such as protein and drug design. We show how models capturing task-specific symmetries lead to improved generalization. We examine the more general question of model mis-specification when the model symmetries don't align with the data symmetries.
  arXiv  Detail & Related papers  (2023-05-27T22:53:37Z)
• Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance [72.50214227616728]
  Interpretability methods are valuable only if their explanations faithfully describe the explained model. We consider neural networks whose predictions are invariant under a specific symmetry group.
  arXiv  Detail & Related papers  (2023-04-13T17:59:03Z)
• On the Strong Correlation Between Model Invariance and Generalization [54.812786542023325]
  Generalization captures a model's ability to classify unseen data. Invariance measures consistency of model predictions on transformations of the data. From a dataset-centric view, we find a certain model's accuracy and invariance linearly correlated on different test sets.
  arXiv  Detail & Related papers  (2022-07-14T17:08:25Z)
• Exploring Local Explanations of Nonlinear Models Using Animated Linear Projections [5.524804393257921]
  We show how to use eXplainable AI (XAI) to shed light on how a model use predictors to arrive at a prediction. To understand how the interaction between predictors affects the variable importance estimate, we can convert LVAs into linear projections. The approach is illustrated with examples from categorical (penguin species, chocolate types) and quantitative (soccer/football salaries, house prices) response models.
  arXiv  Detail & Related papers  (2022-05-11T09:11:02Z)
• Benign-Overfitting in Conditional Average Treatment Effect Prediction with Linear Regression [14.493176427999028]
  We study the benign overfitting theory in the prediction of the conditional average treatment effect (CATE) with linear regression models. We show that the T-learner fails to achieve the consistency except the random assignment, while the IPW-learner converges the risk to zero if the propensity score is known.
  arXiv  Detail & Related papers  (2022-02-10T18:51:52Z)
• Estimation of Bivariate Structural Causal Models by Variational Gaussian Process Regression Under Likelihoods Parametrised by Normalising Flows [74.85071867225533]
  Causal mechanisms can be described by structural causal models. One major drawback of state-of-the-art artificial intelligence is its lack of explainability.
  arXiv  Detail & Related papers  (2021-09-06T14:52:58Z)
• Good Classifiers are Abundant in the Interpolating Regime [64.72044662855612]
  We develop a methodology to compute precisely the full distribution of test errors among interpolating classifiers. We find that test errors tend to concentrate around a small typical value $varepsilon*$, which deviates substantially from the test error of worst-case interpolating model. Our results show that the usual style of analysis in statistical learning theory may not be fine-grained enough to capture the good generalization performance observed in practice.
  arXiv  Detail & Related papers  (2020-06-22T21:12:31Z)
• An Investigation of Why Overparameterization Exacerbates Spurious Correlations [98.3066727301239]
  We identify two key properties of the training data that drive this behavior. We show how the inductive bias of models towards "memorizing" fewer examples can cause over parameterization to hurt.
  arXiv  Detail & Related papers  (2020-05-09T01:59:13Z)
• Decision-Making with Auto-Encoding Variational Bayes [71.44735417472043]
  We show that a posterior approximation distinct from the variational distribution should be used for making decisions. Motivated by these theoretical results, we propose learning several approximate proposals for the best model. In addition to toy examples, we present a full-fledged case study of single-cell RNA sequencing.
  arXiv  Detail & Related papers  (2020-02-17T19:23:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.