Amortized Conditional Independence Testing

• URL: http://arxiv.org/abs/2502.20925v1
• Date: Fri, 28 Feb 2025 10:29:56 GMT
• Title: Amortized Conditional Independence Testing
• Authors: Bao Duong, Nu Hoang, Thin Nguyen,
• Abstract summary: ACID is a transformer-based neural network architecture that learns to test for conditional independence.<n>It consistently achieves state-of-the-art performance against existing baselines under multiple metrics.<n>It is able to generalize robustly to unseen sample sizes, dimensionalities, as well as non-linearities with a remarkably low inference time.
• Score: 6.954510776782872
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Testing for the conditional independence structure in data is a fundamental and critical task in statistics and machine learning, which finds natural applications in causal discovery - a highly relevant problem to many scientific disciplines. Existing methods seek to design explicit test statistics that quantify the degree of conditional dependence, which is highly challenging yet cannot capture nor utilize prior knowledge in a data-driven manner. In this study, an entirely new approach is introduced, where we instead propose to amortize conditional independence testing and devise ACID - a novel transformer-based neural network architecture that learns to test for conditional independence. ACID can be trained on synthetic data in a supervised learning fashion, and the learned model can then be applied to any dataset of similar natures or adapted to new domains by fine-tuning with a negligible computational cost. Our extensive empirical evaluations on both synthetic and real data reveal that ACID consistently achieves state-of-the-art performance against existing baselines under multiple metrics, and is able to generalize robustly to unseen sample sizes, dimensionalities, as well as non-linearities with a remarkably low inference time.

Related papers

• Global Convergence of Continual Learning on Non-IID Data [51.99584235667152]
  We provide a general and comprehensive theoretical analysis for continual learning of regression models. We establish the almost sure convergence results of continual learning under a general data condition for the first time.
  arXiv  Detail & Related papers  (2025-03-24T10:06:07Z)
• Testing Conditional Mean Independence Using Generative Neural Networks [8.323172773256449]
  We introduce a novel population CMI measure and a bootstrap model-based testing procedure.<n>Deep generative neural networks are used to estimate the conditional mean functions involved in the population measure.
  arXiv  Detail & Related papers  (2025-01-28T23:35:24Z)
• How Hard is this Test Set? NLI Characterization by Exploiting Training Dynamics [49.9329723199239]
  We propose a method for the automated creation of a challenging test set without relying on the manual construction of artificial and unrealistic examples. We categorize the test set of popular NLI datasets into three difficulty levels by leveraging methods that exploit training dynamics. When our characterization method is applied to the training set, models trained with only a fraction of the data achieve comparable performance to those trained on the full dataset.
  arXiv  Detail & Related papers  (2024-10-04T13:39:21Z)
• Fast and Reliable Probabilistic Reflectometry Inversion with Prior-Amortized Neural Posterior Estimation [73.81105275628751]
  Finding all structures compatible with reflectometry data is computationally prohibitive for standard algorithms. We address this lack of reliability with a probabilistic deep learning method that identifies all realistic structures in seconds. Our method, Prior-Amortized Neural Posterior Estimation (PANPE), combines simulation-based inference with novel adaptive priors.
  arXiv  Detail & Related papers  (2024-07-26T10:29:16Z)
• Towards Dynamic Causal Discovery with Rare Events: A Nonparametric Conditional Independence Test [4.67306371596399]
  We introduce a novel statistical independence test on data collected from time-invariant systems in which rare but consequential events occur. We provide non-asymptotic sample bounds for the consistency of our method, and validate its performance across various simulated and real-world datasets.
  arXiv  Detail & Related papers  (2022-11-29T21:15:51Z)
• Validation Diagnostics for SBI algorithms based on Normalizing Flows [55.41644538483948]
  This work proposes easy to interpret validation diagnostics for multi-dimensional conditional (posterior) density estimators based on NF. It also offers theoretical guarantees based on results of local consistency. This work should help the design of better specified models or drive the development of novel SBI-algorithms.
  arXiv  Detail & Related papers  (2022-11-17T15:48:06Z)
• Model-Free Sequential Testing for Conditional Independence via Testing by Betting [8.293345261434943]
  The proposed test allows researchers to analyze an incoming i.i.d. data stream with any arbitrary dependency structure. We allow the processing of data points online as soon as they arrive and stop data acquisition once significant results are detected.
  arXiv  Detail & Related papers  (2022-10-01T20:05:33Z)
• Conditional Independence Testing via Latent Representation Learning [2.566492438263125]
  LCIT (Latent representation based Conditional Independence Test) is a novel non-parametric method for conditional independence testing based on representation learning. Our main contribution involves proposing a generative framework in which to test for the independence between X and Y given Z.
  arXiv  Detail & Related papers  (2022-09-04T07:16:03Z)
• A Simple Unified Approach to Testing High-Dimensional Conditional Independences for Categorical and Ordinal Data [0.26651200086513094]
  Conditional independence (CI) tests underlie many approaches to model testing and structure learning in causal inference. Most existing CI tests for categorical and ordinal data stratify the sample by the conditioning variables, perform simple independence tests in each stratum, and combine the results. Here we propose a simple unified CI test for ordinal and categorical data that maintains reasonable calibration and power in high dimensions.
  arXiv  Detail & Related papers  (2022-06-09T08:56:12Z)
• Amortized Inference for Causal Structure Learning [72.84105256353801]
  Learning causal structure poses a search problem that typically involves evaluating structures using a score or independence test. We train a variational inference model to predict the causal structure from observational/interventional data. Our models exhibit robust generalization capabilities under substantial distribution shift.
  arXiv  Detail & Related papers  (2022-05-25T17:37:08Z)
• Stateful Offline Contextual Policy Evaluation and Learning [88.9134799076718]
  We study off-policy evaluation and learning from sequential data. We formalize the relevant causal structure of problems such as dynamic personalized pricing. We show improved out-of-sample policy performance in this class of relevant problems.
  arXiv  Detail & Related papers  (2021-10-19T16:15:56Z)
• Causal Feature Selection for Algorithmic Fairness [61.767399505764736]
  We consider fairness in the integration component of data management. We propose an approach to identify a sub-collection of features that ensure the fairness of the dataset.
  arXiv  Detail & Related papers  (2020-06-10T20:20:10Z)

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.