A new method reduces CI tests for causal structure learning.
problem Exponential CI tests in constraint-based methods.
method Recursive Markov boundary-based approach.
result Significantly reduces CI tests compared to existing methods.
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48 results for “CI tests reduction”
Approximate Kernel-based Conditional Independence Tests for Fast Non-Parametric Causal Discoverystat.ME
Constraint-based causal discovery (CCD) algorithms require fast and accurate conditional independence (CI) testing. The Kernel Conditional Independence Test (KCIT) is currently one of the most popular CI tests in the non-parametric setting, but many investigators cannot use KCIT with large datasets because the test sca…
Conditional Independence Testing with Heteroskedastic Data and Applications to Causal Discoverystat.ME
Improved CI test for heteroskedastic data enhances causal discovery.
problem CI testing assumptions fail in heteroskedastic data.
method Adapted partial correlation CI test for heteroskedastic noise.
result The adapted test outperforms standard CI test in heteroskedastic cases.
Private CI tests for continuous Z with privacy constraints.
problem Testing conditional independence under differential privacy constraints.
method Developed two private CI testing procedures based on generalized covariance and conditional randomization tests.
result First private CI tests with rigorous theoretical guarantees for continuous Z.
Polynomial delay algorithm tests causal models with hidden variables.
problem Testing causal models with hidden variables in polynomial delay.
method c-component local Markov property (C-LMP) and polynomial delay algorithm.
result First algorithm for poly-delay testing of CIs in causal graphs with hidden variables.
PAIR-CI: Calibrated Conditional Independence Testing for Causal Discovery with Incomplete Datastat.ME
PAIR-CI calibrates CI tests for causal discovery with incomplete data.
problem Miscalibration of CI tests when imputing incomplete data.
method Integrates multiple imputation directly into the inferential procedure via a paired permutation design.
result PAIR-CI reduces false positive rates to below 5% in simulations.
A new CI test avoids information loss in discretized data.
problem Incorrect CI conclusions from discretized data.
method Proposes a sample-efficient CI test using GMM and nodewise regression.
result Derives an accurate test statistic and establishes its asymptotic distribution.
New methods for CI testing under model misspecification.
problem Challenges in CI testing with misspecified models.
method Proposes new approximations and upper bounds for testing errors of regression-based CI tests.
result Introduces the Rao-Blackwellized Predictor Test (RBPT) robust against misspecified inductive biases.
A new kernel-based CI test improves on existing methods.
problem Testing conditional independence (CI) in a broad range of dependencies.
method Regression-model-agnostic kernel-based CI test using reproducing kernel Hilbert spaces.
result GKCM outperforms state-of-the-art CI tests in simulations.
The paper develops CI tests for causal discovery in SDEs.
problem Inferring causal structure from stochastic dynamical systems.
method Developed CI constraints and a CI test for SDEs.
result Proposed CI test outperforms existing methods.
Given independent samples generated from the joint distribution , we study the problem of Conditional Independence (CI-Testing), i.e., whether the joint equals the CI distribution $p^{CI}(\mathbf{x},\mathbf{y},\mathbf{z})= p(\mathbf{z}) p(\mathbf{y}|\mathbf{z})p(\mathbf{x}|\mathbf{z…
Unified CI test for categorical and ordinal data maintains power in high dimensions.
problem Rapid degradation of statistical power in existing CI tests for high-dimensional conditioning variables.
method Unified CI test for categorical and ordinal data, maintaining reasonable calibration and power in high dimensions.
result Our test outperforms existing baselines in model testing and structure learning for dense directed graphical models.
We study the statistical properties of an estimator derived by applying a gradient ascent method with multiple initializations to a multi-modal likelihood function. We derive the population quantity that is the target of this estimator and study the properties of confidence intervals (CIs) constructed from asymptotic n…
Algorithm recovers causal graphs from data with fewer tests.
problem Recovering causal graphs from observational data with latent confounders and selection bias.
method Iterative CI tests with increasing condition set sizes.
result Significantly fewer CI tests and condition sets compared to FCI.
A new algorithm reduces CI tests for causal graph recovery.
problem Exponential CI tests limit causal discovery algorithms.
method CCPG (Causal Consistent Partition Graph) with polynomial CI tests.
result CCPG efficiently recovers causal graph with polynomial tests.
FMCIT accelerates CI tests for causal discovery, maintaining power and efficiency.
problem High computational complexity in CI tests limits practical applicability of causal discovery methods.
method Flow Matching-based Conditional Independence Test (FMCIT) that leverages flow matching for fast CI tests.
result FMCIT effectively controls type-I error and maintains high testing power under the alternative hypothesis.
We consider the problem of non-parametric Conditional Independence testing (CI testing) for continuous random variables. Given i.i.d samples from the joint distribution of continuous random vectors and we determine whether . We approach this by converting the conditional independenc…
Conditional independence tests (CI tests) have received special attention lately in Machine Learning and Computational Intelligence related literature as an important indicator of the relationship among the variables used by their models. In the field of Probabilistic Graphical Models (PGM)--which includes Bayesian Net…
New insights into CI tests reveal key factors for practical performance.
problem Understanding and improving CI tests in practical applications.
method Investigation of the Kernel-based Conditional Independence (KCI) test and analysis of its practical behavior.
result Errors in conditional mean embedding estimates and appropriate conditioning kernel selection are crucial for CI tests.
New research shows CI in few-shot learning is misleading due to sampling with replacement.
problem Misleading confidence intervals in few-shot learning due to sampling with replacement.
method Comparative analysis of CIs computed with and without replacement.
result Significant underestimation of CI by the predominant method.
New method tests conditional independence using spectral representations.
problem Untestable conditional independence in many settings.
method Spectral representations of partial covariance operators, bi-level contrastive learning.
result Asymptotic validity and power guarantees for CI testing.
We consider the problem of learning causal relationships from relational data. Existing approaches rely on queries to a relational conditional independence (RCI) oracle to establish and orient causal relations in such a setting. In practice, queries to a RCI oracle have to be replaced by reliable tests for RCI against …
Continuous integration is an indispensable step of modern software engineering practices to systematically manage the life cycles of system development. Developing a machine learning model is no difference - it is an engineering process with a life cycle, including design, implementation, tuning, testing, and deploymen…
The Dual PC Algorithm and the Role of Gaussianity for Structure Learning of Bayesian Networksstat.ML
Dual PC algorithm improves structure learning of Bayesian networks.
problem Learning the structure of Bayesian networks from observational data.
method Dual PC algorithm, leveraging covariance and precision matrices, and partial correlations.
result The dual PC algorithm outperforms the classic PC algorithm in structure recovery, even with non-Gaussian data.
New method uses CDMs to improve CI testing without distributional assumptions.
problem Testing conditional independence when the conditional distribution is unknown.
method Uses conditional diffusion models (CDMs) to approximate and a classifier-based CMI estimator.
result Proposed method performs better than GAN-based CI tests and controls type I and II errors.
The paper provides CI for test unfairness of group-fairness-aware classifiers trained with online SGD.
problem Ensuring fairness in machine learning models trained with stochastic gradient descent.
method Developed an online multiplier bootstrap method to estimate CI for test unfairness of DI and DM-aware linear classifiers.
result Asymptotic Central Limit Theorem holds for CI estimation of DI and DM-aware models.
Estimates conditional mutual information using a minmax formulation.
problem Estimating conditional mutual information in high dimensions.
method Uses a minmax optimization problem to train a neural network.
result Improves estimation accuracy compared to existing methods.
The paper analyzes the power of MX CI tests and finds likelihood-based statistics most powerful.
problem Testing conditional independence under model-X assumptions.
method Conditional randomization test (CRT) and MX knockoffs.
result Likelihood-based statistics are most powerful in MX CI tests.
Paper improves CI and CS for bounded means using betting and mixtures.
problem Estimating means of bounded random variables.
method Composite nonnegative martingales, testing by betting, method of mixtures.
result Empirically outperforms existing CI and CS methods.
Predictive models identify patients at risk of severe COVID-19.
problem Identifying patients at risk of severe COVID-19 to ease healthcare strain.
method Machine learning on routinely collected clinical data.
result Models predict positive SARS-CoV-2 tests, hospitalizations, and critical care with high accuracy.
Algorithm recovers causal graphs in presence of latent confounders and selection bias.
problem Recovering causal graphs in the presence of latent confounders and selection bias.
method Iterative causal discovery (ICD) algorithm that relies on causal Markov and faithfulness assumptions.
result Sound and complete algorithm that recovers the equivalence class of the underlying causal graph.
Recursive causal discovery reduces errors and complexity in causal graph learning.
problem Challenges in causal discovery from limited data and computational complexity.
method Removable variables for recursive causal discovery, reducing problem size and CI tests.
result Worst-case performances nearly match lower bound, with state-of-the-art efficiency.
Many real datasets contain values missing not at random (MNAR). In this scenario, investigators often perform list-wise deletion, or delete samples with any missing values, before applying causal discovery algorithms. List-wise deletion is a sound and general strategy when paired with algorithms such as FCI and RFCI, b…
We analyze anomaly detection class imbalance using a solvable model.
problem Class imbalance hampers anomaly detection performance.
method We use an exact solution of the teacher-student perceptron model through replica theory.
result Optimal train imbalance is often different from 50%, influenced by intrinsic imbalance and data abundance.
Algorithm learns causal structures from low-order conditional independencies.
problem Estimating high-order conditional independencies from data is challenging.
method Proposes an algorithm to compute a faithful graphical representation from low-order conditional independencies.
result Algorithm successfully learns causal structures from zero- and first-order conditional independencies.
The betting CI outperforms classical methods in constructing confidence intervals for bounded means.
problem Constructing nonasymptotic confidence intervals for bounded means.
method A betting-based approach to define and time-uniform variants of confidence intervals (CSs).
result The betting CI matches the fundamental limits, outperforming existing empirical Bernstein CIs.
Study designs experiments to identify causal graph structure with cycles and latent confounders.
problem Identify causal graph structure with cycles and latent confounders.
method Established lower bounds, developed CI and do see tests algorithms, and proved tightness.
result Proposed algorithms can recover all causal edges except for double adjacent bidirected edges.
Markov blanket feature selection, while theoretically optimal, is generally challenging to implement. This is due to the shortcomings of existing approaches to conditional independence (CI) testing, which tend to struggle either with the curse of dimensionality or computational complexity. We propose a novel two-step a…
Counterfactuals help compare different scenarios in CI and XAI.
problem Comparing how counterfactuals are used in causal inference and explainable AI.
method Formal definition and comparison of counterfactuals in CI and XAI.
result Identified opportunities for cross-fertilization between CI and XAI.
Optimizes COVID-19 testing policy using a Multi-Armed Bandit approach.
problem Balancing discovery of positive cases with population surveillance.
method Risk scoring and random sampling based on Multi-Armed Bandit theory.
result Effective prioritization captures 65-92% of positive cases with varying testing capacity.
Novel method discovers causal relations in time series data, even with autocorrelation.
problem Discovering causal relations in time series data with strong autocorrelation.
method Conditional independence (CI) based PCMCI method, optimized for contemporaneous and lagged links.
result PCMCI outperforms other methods in detecting causal links and controlling false positives.
A machine learning configuration refers to a combination of preprocessor, learner, and hyperparameters. Given a set of configurations and a large dataset randomly split into training and testing set, we study how to efficiently select the best configuration with approximately the highest testing accuracy when trained f…
Constructing Confidence Intervals for 'the' Generalization Error -- a Comprehensive Benchmark Studystat.ML
Study evaluates 13 CI methods for generalization error across 19 problems.
problem Evaluating prediction model quality using confidence intervals for generalization error.
method Compared 13 CI methods on 19 tabular regression and classification problems using cross-validation and bootstrapping.
result Identified a subset of methods with good coverage, width, and runtime.
AR CI framework handles complex confounders and sequential actions.
problem Low-dimensional confounders and singleton actions in causal inference.
method Sequencification to transform data into sequences, enabling CI with complex confounders and sequential actions.
result AR model can estimate multiple causal quantities using a single model, simplifying inference and improving outcome prediction.
Bayesian logistic regression improves clinical risk prediction models over time.
problem Improving clinical risk prediction models after deployment to adapt to temporal shifts.
method Bayesian logistic regression (BLR) and Markov variant (MarBLR) for online recalibration and revision of prediction models.
result BLR and MarBLR consistently outperform static models and other online revision methods, improving average AUC and calibration index.
FAQ efficiently evaluates LLMs with statistical guarantees using adaptive query selection.
problem Efficiently evaluating many LLMs on a large suite of benchmarks is expensive.
method FAQ uses Bayesian factor models, adaptive sampling, and proactive active inference to select queries.
result FAQ delivers up to 5x effective sample size gains over baselines, matching CI width with fewer queries.
CI-GNN uses GNNs to diagnose psychiatric disorders by identifying causally relevant brain regions.
problem Leveraging GNNs for psychiatric diagnosis requires interpretable models to understand decision-making.
method CI-GNN integrates Granger causality into GNNs to identify causally relevant subgraphs.
result CI-GNN provides more reliable and concise explanations of psychiatric diagnoses.
Diabetic eye disease is one of the fastest growing causes of preventable blindness. With the advent of anti-VEGF (vascular endothelial growth factor) therapies, it has become increasingly important to detect center-involved diabetic macular edema (ci-DME). However, center-involved diabetic macular edema is diagnosed us…