Inferring deterministic causal relations

Abstract

We consider two variables that are related to each other by an invertible function. While it has previously been shown that the dependence structure of the noise can provide hints to determine which of the two variables is the cause, we presently show that even in the deterministic (noise-free) case, there are asymmetries that can be exploited for causal inference. Our method is based on the idea that if the function and the probability density of the cause are chosen independently, then the distribution of the effect will, in a certain sense, depend on the function. We provide a theoretical analysis of this method, showing that it also works in the low noise regime, and link it to information geometry. We report strong empirical results on various real-world data sets from different domains.

Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI2010)

References in corpus (3)

• Estimating Mutual Information
• On the Identifiability of the Post-Nonlinear Causal Model
• Telling cause from effect based on high-dimensional observations

Cited by in corpus (31)

• Causality matters in medical imaging
• Distinguishing cause from effect using observational data: methods and benchmarks
• Causality for Machine Learning
• Domain Generalization via Multidomain Discriminant Analysis
• Analysis of cause-effect inference by comparing regression errors
• Neural Network Attributions: A Causal Perspective
• Sparse Linear Identifiable Multivariate Modeling
• Testing whether linear equations are causal: A free probability theory approach
• Nonlinear Invariant Risk Minimization: A Causal Approach
• Telling cause from effect in deterministic linear dynamical systems
• Generalization in anti-causal learning
• From Dependence to Causation
• Semi-Supervised Learning, Causality and the Conditional Cluster Assumption
• Beta Power May Mediate the Effect of Gamma-TACS on Motor Performance
• SADA: A General Framework to Support Robust Causation Discovery with Theoretical Guarantee
• Robust Learning via Cause-Effect Models
• A Bayesian Model for Bivariate Causal Inference
• Latent Instrumental Variables as Priors in Causal Inference based on Independence of Cause and Mechanism
• Leveraging directed causal discovery to detect latent common causes
• Merging joint distributions via causal model classes with low VC dimension
• Reparametrization Invariance in non-parametric Causal Discovery
• The Effect of Noise Level on Causal Identification with Additive Noise Models
• Independent mechanism analysis, a new concept?
• Causal Regularization
• Causal Inference in Geoscience and Remote Sensing from Observational Data
• Information-Theoretic Approximation to Causal Models
• Gradient-based Causal Structure Learning with Normalizing Flow
• Causal Inference in Geosciences with Kernel Sensitivity Maps
• Causal Discovery in Linear Structural Causal Models with Deterministic Relations
• Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP
• Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations