A Sample Efficient Conditional Independence Test in the Presence of Discretization

• URL: http://arxiv.org/abs/2506.08747v1
• Date: Tue, 10 Jun 2025 12:41:26 GMT
• Title: A Sample Efficient Conditional Independence Test in the Presence of Discretization
• Authors: Boyang Sun, Yu Yao, Xinshuai Dong, Zongfang Liu, Tongliang Liu, Yumou Qiu, Kun Zhang,
• Abstract summary: 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.
• Score: 54.047334792855345
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In many real-world scenarios, interested variables are often represented as discretized values due to measurement limitations. Applying Conditional Independence (CI) tests directly to such discretized data, however, can lead to incorrect conclusions. To address this, recent advancements have sought to infer the correct CI relationship between the latent variables through binarizing observed data. However, this process inevitably results in a loss of information, which degrades the test's performance. Motivated by this, this paper introduces a sample-efficient CI test that does not rely on the binarization process. We find that the independence relationships of latent continuous variables can be established by addressing an over-identifying restriction problem with Generalized Method of Moments (GMM). Based on this insight, we derive an appropriate test statistic and establish its asymptotic distribution correctly reflecting CI by leveraging nodewise regression. Theoretical findings and Empirical results across various datasets demonstrate that the superiority and effectiveness of our proposed test. Our code implementation is provided in https://github.com/boyangaaaaa/DCT

Related papers

• Permutation-Based Rank Test in the Presence of Discretization and Application in Causal Discovery with Mixed Data [16.892960387325743]
  In psychometric studies, the continuous level of certain personality dimensions of a person can only be measured after discretization.<n>We propose Mixed data Permutation-based Rank Test (MPRT), which properly controls the statistical errors even when some variables are discretized.<n>MPRT can effectively control the Type I error in the presence of discretization while previous methods cannot.
  arXiv  Detail & Related papers  (2025-01-31T09:47:26Z)
• 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)
• On the Identification of Temporally Causal Representation with Instantaneous Dependence [50.14432597910128]
  Temporally causal representation learning aims to identify the latent causal process from time series observations. Most methods require the assumption that the latent causal processes do not have instantaneous relations. We propose an textbfIDentification framework for instantanetextbfOus textbfLatent dynamics.
  arXiv  Detail & Related papers  (2024-05-24T08:08:05Z)
• Causal Discovery via Conditional Independence Testing with Proxy Variables [35.3493980628004]
  The presence of unobserved variables, such as the latent confounder, can introduce bias in conditional independence testing. We propose a novel hypothesis-testing procedure that can effectively examine the existence of the causal relationship over continuous variables.
  arXiv  Detail & Related papers  (2023-05-09T09:08:39Z)
• 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)
• Sequential Kernelized Independence Testing [77.237958592189]
  We design sequential kernelized independence tests inspired by kernelized dependence measures.<n>We demonstrate the power of our approaches on both simulated and real data.
  arXiv  Detail & Related papers  (2022-12-14T18:08:42Z)
• 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)
• Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations [84.42837346400151]
  Estimating counterfactual outcomes over time has the potential to unlock personalized healthcare. Existing causal inference approaches consider regular, discrete-time intervals between observations and treatment decisions. We propose a controllable simulation environment based on a model of tumor growth for a range of scenarios.
  arXiv  Detail & Related papers  (2022-06-16T17:15:15Z)
• Nonparametric Conditional Local Independence Testing [69.31200003384122]
  Conditional local independence is an independence relation among continuous time processes. No nonparametric test of conditional local independence has been available. We propose such a nonparametric test based on double machine learning.
  arXiv  Detail & Related papers  (2022-03-25T10:31:02Z)
• Testing for Outliers with Conformal p-values [14.158078752410182]
  The goal is to test whether new independent samples belong to the same distribution as a reference data set or are outliers. We propose a solution based on conformal inference, a broadly applicable framework which yields p-values that are marginally valid but mutually dependent for different test points. We prove these p-values are positively dependent and enable exact false discovery rate control, although in a relatively weak marginal sense.
  arXiv  Detail & Related papers  (2021-04-16T17:59:21Z)
• Stable Prediction via Leveraging Seed Variable [73.9770220107874]
  Previous machine learning methods might exploit subtly spurious correlations in training data induced by non-causal variables for prediction. We propose a conditional independence test based algorithm to separate causal variables with a seed variable as priori, and adopt them for stable prediction. Our algorithm outperforms state-of-the-art methods for stable prediction.
  arXiv  Detail & Related papers  (2020-06-09T06:56:31Z)

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.