Causal Discovery

insight - Causal Discovery

Identifying Causal Relationships between Continuous Variables using Conditional Independence Testing with Proxy Variables

A novel hypothesis-testing procedure that can effectively identify causal relationships between continuous variables, without any parametric assumptions, by leveraging proxy variables to adjust for the bias caused by unobserved confounders.

Causality-Aware Local Interpretable Model-Agnostic Explanations: Enhancing Transparency and Reliability of Black-Box Model Explanations

Integrating causal knowledge into local model-agnostic explanations to enhance transparency and reliability of black-box model decisions.

Effiziente Causal Discovery in Stochastischen Prozessen mit Signature Kernel Conditional Independence Tests

Effiziente Causal Discovery in stochastischen Prozessen durch Signature Kernel Tests.

Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes: A Comprehensive Analysis

Studying the effectiveness of a kernel-based conditional independence test in causal discovery for stochastic processes.

Trust Your Gradient: A Novel Approach to Causal Discovery

The author introduces a novel method, GIT, that leverages gradient estimators for targeted intervention in causal discovery.

Signature Kernel Conditional Independence Tests for Causal Discovery in Stochastic Processes

The author develops a kernel-based test of conditional independence on path-space using signature kernels, demonstrating superior performance compared to existing approaches. The approach enables constraint-based causal discovery in acyclic stochastic dynamical systems.

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