Testing for Causal Fairness

• URL: http://arxiv.org/abs/2502.12874v1
• Date: Tue, 18 Feb 2025 14:05:04 GMT
• Title: Testing for Causal Fairness
• Abstract summary: Causality is widely used in fairness analysis to prevent discrimination on sensitive attributes, such as genders in career recruitment and races in crime prediction.<n>The current data-based Potential Outcomes Framework (POF) often leads to untrustworthy fairness analysis results when handling high-dimensional data.<n>We introduce a distribution-based POF that transform fairness analysis into Distributional Closeness Testing (DCT) by intervening on sensitive attributes.
• Score: 10.105031765942437
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Causality is widely used in fairness analysis to prevent discrimination on sensitive attributes, such as genders in career recruitment and races in crime prediction. However, the current data-based Potential Outcomes Framework (POF) often leads to untrustworthy fairness analysis results when handling high-dimensional data. To address this, we introduce a distribution-based POF that transform fairness analysis into Distributional Closeness Testing (DCT) by intervening on sensitive attributes. We define counterfactual closeness fairness as the null hypothesis of DCT, where a sensitive attribute is considered fair if its factual and counterfactual potential outcome distributions are sufficiently close. We introduce the Norm-Adaptive Maximum Mean Discrepancy Treatment Effect (N-TE) as a statistic for measuring distributional closeness and apply DCT using the empirical estimator of NTE, referred to Counterfactual Fairness-CLOseness Testing ($\textrm{CF-CLOT}$). To ensure the trustworthiness of testing results, we establish the testing consistency of N-TE through rigorous theoretical analysis. $\textrm{CF-CLOT}$ demonstrates sensitivity in fairness analysis through the flexibility of the closeness parameter $\epsilon$. Unfair sensitive attributes have been successfully tested by $\textrm{CF-CLOT}$ in extensive experiments across various real-world scenarios, which validate the consistency of the testing.

Related papers

• Distributionally Faithful Imputation via Positive Semi-Definite Kernel Density Estimation [55.47411235954028]
  We recast imputation under missing completely at random as density estimation from masked observations.<n>We obtain a convex empirical risk problem with closed form marginals, solvable by a Newton interior point method.<n>The resulting PSD Impute model yields both single and multiple imputations from the same fitted density.
  arXiv  Detail & Related papers  (2026-07-08T15:51:05Z)
• FACTOR: Counterfactual Training-Free Test-Time Adaptation for Open-Vocabulary Object Detection [63.91351553178842]
  FACTOR is a training-free Test-time adaptation for Open-vocabulary object detection.<n>It quantifies attribute sensitivity, semantic relevance, and prediction variation to selectively suppress attribute-dependent predictions.<n>Experiments on PASCAL-C, COCO-C, and FoggyCityscapes show that FACTOR consistently outperforms prior TTA methods.
  arXiv  Detail & Related papers  (2026-05-05T02:31:18Z)
• GAAVI: Global Asymptotic Anytime Valid Inference for the Conditional Mean Function [40.13687327412775]
  We provide a novel anytime-valid test for a CMF global null.<n>We show how to construct function-valued confidence for the CMF and contrasts thereof.<n>Our method is well-powered across various distributions while preserving the nominal error rate under continuous monitoring.
  arXiv  Detail & Related papers  (2026-02-08T19:34:18Z)
• COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees [51.5976496056012]
  COIN is an uncertainty-guarding selection framework that calibrates statistically valid thresholds to filter a single generated answer per question.<n>COIN estimates the empirical error rate on a calibration set and applies confidence interval methods to establish a high-probability upper bound on the true error rate.<n>We demonstrate COIN's robustness in risk control, strong test-time power in retaining admissible answers, and predictive efficiency under limited calibration data.
  arXiv  Detail & Related papers  (2025-06-25T07:04:49Z)
• A Sample Efficient Conditional Independence Test in the Presence of Discretization [54.047334792855345]
  Conditional Independence (CI) tests directly to discretized data can lead to incorrect conclusions.<n>Recent advancements have sought to infer the correct CI relationship between the latent variables through binarizing observed data.<n>Motivated by this, this paper introduces a sample-efficient CI test that does not rely on the binarization process.
  arXiv  Detail & Related papers  (2025-06-10T12:41:26Z)
• Testing Fairness with Utility Tradeoffs: A Wasserstein Projection Approach [6.378410364292642]
  We propose a statistical hypothesis testing framework that jointly evaluates approximate fairness and utility.<n>Our framework builds on the strong demographic parity criterion and incorporates a utility measure motivated by the potential outcomes framework.<n>We show that the test is computationally tractable, interpretable, broadly applicable across machine learning models, and extendable to more general settings.
  arXiv  Detail & Related papers  (2025-05-16T20:29:06Z)
• Toward Fair Federated Learning under Demographic Disparities and Data Imbalance [8.444310568786408]
  Federated learning (FL) enables privacy-preserving collaboration across institutions.<n>We propose FedIDA, a framework-agnostic method that combines fairness-aware regularization with group-conditional oversampling.<n>We show that FedIDA consistently improves fairness while maintaining competitive predictive performance.
  arXiv  Detail & Related papers  (2025-05-14T11:22:54Z)
• $t$-Testing the Waters: Empirically Validating Assumptions for Reliable A/B-Testing [3.988614978933934]
  A/B-tests are a cornerstone of experimental design on the web, with wide-ranging applications and use-cases.<n>We propose a practical method to test whether the $t$-test's assumptions are met, and the A/B-test is valid.<n>This provides an efficient and effective way to empirically assess whether the $t$-test's assumptions are met, and the A/B-test is valid.
  arXiv  Detail & Related papers  (2025-02-07T09:55:24Z)
• Robust Conformal Prediction under Distribution Shift via Physics-Informed Structural Causal Model [24.58531056536442]
  Conformal prediction (CP) handles uncertainty by predicting a set on a test input. This coverage can be guaranteed on test data even if the marginal distributions $P_X$ differ between calibration and test datasets. We propose a physics-informed structural causal model (PI-SCM) to reduce the upper bound.
  arXiv  Detail & Related papers  (2024-03-22T08:13:33Z)
• Uncertainty-Calibrated Test-Time Model Adaptation without Forgetting [55.17761802332469]
  Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and test data by adapting a given model w.r.t. any test sample. Prior methods perform backpropagation for each test sample, resulting in unbearable optimization costs to many applications. We propose an Efficient Anti-Forgetting Test-Time Adaptation (EATA) method which develops an active sample selection criterion to identify reliable and non-redundant samples.
  arXiv  Detail & Related papers  (2024-03-18T05:49:45Z)
• Equal Opportunity of Coverage in Fair Regression [50.76908018786335]
  We study fair machine learning (ML) under predictive uncertainty to enable reliable and trustworthy decision-making. We propose Equal Opportunity of Coverage (EOC) that aims to achieve two properties: (1) coverage rates for different groups with similar outcomes are close, and (2) the coverage rate for the entire population remains at a predetermined level.
  arXiv  Detail & Related papers  (2023-11-03T21:19:59Z)
• Fair-CDA: Continuous and Directional Augmentation for Group Fairness [48.84385689186208]
  We propose a fine-grained data augmentation strategy for imposing fairness constraints. We show that group fairness can be achieved by regularizing the models on transition paths of sensitive features between groups. Our proposed method does not assume any data generative model and ensures good generalization for both accuracy and fairness.
  arXiv  Detail & Related papers  (2023-04-01T11:23:00Z)
• Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach [72.19525160912943]
  We first theoretically demonstrate the inherent connection between distribution shift, data perturbation, and model weight perturbation. We then analyze the sufficient conditions to guarantee fairness for the target dataset. Motivated by these sufficient conditions, we propose robust fairness regularization (RFR)
  arXiv  Detail & Related papers  (2023-03-06T17:19:23Z)
• Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated Uncertainty [52.03490691733464]
  We introduce DEviS, an easily implementable foundational model that seamlessly integrates into various medical image segmentation networks. By leveraging subjective logic theory, we explicitly model probability and uncertainty for the problem of medical image segmentation. DeviS incorporates an uncertainty-aware filtering module, which utilizes the metric of uncertainty-calibrated error to filter reliable data.
  arXiv  Detail & Related papers  (2023-01-01T05:02:46Z)
• Near-Optimal Non-Parametric Sequential Tests and Confidence Sequences with Possibly Dependent Observations [44.71254888821376]
  We provide the first type-I-error and expected-rejection-time guarantees under general non-data generating processes. We show how to apply our results to inference on parameters defined by estimating equations, such as average treatment effects.
  arXiv  Detail & Related papers  (2022-12-29T18:37:08Z)
• Leveraging Unlabeled Data to Predict Out-of-Distribution Performance [63.740181251997306]
  Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions. In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data. We propose Average Thresholded Confidence (ATC), a practical method that learns a threshold on the model's confidence, predicting accuracy as the fraction of unlabeled examples.
  arXiv  Detail & Related papers  (2022-01-11T23:01:12Z)
• Fair Normalizing Flows [10.484851004093919]
  We present Fair Normalizing Flows (FNF), a new approach offering more rigorous fairness guarantees for learned representations. The main advantage of FNF is that its exact likelihood computation allows us to obtain guarantees on the maximum unfairness of any potentially adversarial downstream predictor. We experimentally demonstrate the effectiveness of FNF in enforcing various group fairness notions, as well as other attractive properties such as interpretability and transfer learning.
  arXiv  Detail & Related papers  (2021-06-10T17:35:59Z)
• Testing Group Fairness via Optimal Transport Projections [12.972104025246091]
  The proposed test is a flexible, interpretable, and statistically rigorous tool for auditing whether exhibited biases are to the perturbation or due to the randomness in the data. The statistical challenges, which may arise from multiple impact criteria that define group fairness, are conveniently tackled by projecting the empirical measure onto the set of group-fair probability models. The proposed framework can also be used to test for testing composite intrinsic fairness hypotheses and fairness with multiple sensitive attributes.
  arXiv  Detail & Related papers  (2021-06-02T10:51:39Z)

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.