Learning Counterfactually Invariant Predictors

• URL: http://arxiv.org/abs/2207.09768v4
• Date: Fri, 9 Aug 2024 09:38:07 GMT
• Title: Learning Counterfactually Invariant Predictors
• Authors: Francesco Quinzan, Cecilia Casolo, Krikamol Muandet, Yucen Luo, Niki Kilbertus,
• Abstract summary: We propose a model-agnostic framework, called Counterfactually Invariant Prediction (CIP) Our experimental results demonstrate the effectiveness of CIP in enforcing counterfactual invariance across various simulated and real-world datasets.
• Score: 11.682403472580162
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Notions of counterfactual invariance (CI) have proven essential for predictors that are fair, robust, and generalizable in the real world. We propose graphical criteria that yield a sufficient condition for a predictor to be counterfactually invariant in terms of a conditional independence in the observational distribution. In order to learn such predictors, we propose a model-agnostic framework, called Counterfactually Invariant Prediction (CIP), building on the Hilbert-Schmidt Conditional Independence Criterion (HSCIC), a kernel-based conditional dependence measure. Our experimental results demonstrate the effectiveness of CIP in enforcing counterfactual invariance across various simulated and real-world datasets including scalar and multi-variate settings.

Related papers

• Probabilistic Conformal Prediction with Approximate Conditional Validity [81.30551968980143]
  We develop a new method for generating prediction sets that combines the flexibility of conformal methods with an estimate of the conditional distribution. Our method consistently outperforms existing approaches in terms of conditional coverage.
  arXiv  Detail & Related papers  (2024-07-01T20:44:48Z)
• Conformal Predictive Systems Under Covariate Shift [2.9310590399782788]
  Conformal Predictive Systems (CPS) offer a versatile framework for constructing predictive distributions. We propose weighted CPS, akin to weighted Conformal Prediction (WCP) We present theoretical underpinnings and conjectures regarding the validity and efficacy of WCPS.
  arXiv  Detail & Related papers  (2024-04-23T13:23:27Z)
• Selective Nonparametric Regression via Testing [54.20569354303575]
  We develop an abstention procedure via testing the hypothesis on the value of the conditional variance at a given point. Unlike existing methods, the proposed one allows to account not only for the value of the variance itself but also for the uncertainty of the corresponding variance predictor.
  arXiv  Detail & Related papers  (2023-09-28T13:04:11Z)
• Invariant Probabilistic Prediction [45.90606906307022]
  We show that arbitrary distribution shifts do not, in general, admit invariant and robust probabilistic predictions. We propose a method to yield invariant probabilistic predictions, called IPP, and study the consistency of the underlying parameters.
  arXiv  Detail & Related papers  (2023-09-18T18:50:24Z)
• Federated Conformal Predictors for Distributed Uncertainty Quantification [83.50609351513886]
  Conformal prediction is emerging as a popular paradigm for providing rigorous uncertainty quantification in machine learning. In this paper, we extend conformal prediction to the federated learning setting. We propose a weaker notion of partial exchangeability, better suited to the FL setting, and use it to develop the Federated Conformal Prediction framework.
  arXiv  Detail & Related papers  (2023-05-27T19:57:27Z)
• Distribution-Free Finite-Sample Guarantees and Split Conformal Prediction [0.0]
  split conformal prediction represents a promising avenue to obtain finite-sample guarantees under minimal distribution-free assumptions. We highlight the connection between split conformal prediction and classical tolerance predictors developed in the 1940s.
  arXiv  Detail & Related papers  (2022-10-26T14:12:24Z)
• CovarianceNet: Conditional Generative Model for Correct Covariance Prediction in Human Motion Prediction [71.31516599226606]
  We present a new method to correctly predict the uncertainty associated with the predicted distribution of future trajectories. Our approach, CovariaceNet, is based on a Conditional Generative Model with Gaussian latent variables.
  arXiv  Detail & Related papers  (2021-09-07T09:38:24Z)
• Robust Validation: Confident Predictions Even When Distributions Shift [19.327409270934474]
  We describe procedures for robust predictive inference, where a model provides uncertainty estimates on its predictions rather than point predictions. We present a method that produces prediction sets (almost exactly) giving the right coverage level for any test distribution in an $f$-divergence ball around the training population. An essential component of our methodology is to estimate the amount of expected future data shift and build robustness to it.
  arXiv  Detail & Related papers  (2020-08-10T17:09:16Z)
• Balance-Subsampled Stable Prediction [55.13512328954456]
  We propose a novel balance-subsampled stable prediction (BSSP) algorithm based on the theory of fractional factorial design. A design-theoretic analysis shows that the proposed method can reduce the confounding effects among predictors induced by the distribution shift. Numerical experiments on both synthetic and real-world data sets demonstrate that our BSSP algorithm significantly outperforms the baseline methods for stable prediction across unknown test data.
  arXiv  Detail & Related papers  (2020-06-08T07:01:38Z)

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.