Inferring Causal Direction from Observational Data: A Complexity Approach

• URL: http://arxiv.org/abs/2010.05635v1
• Date: Mon, 12 Oct 2020 12:10:37 GMT
• Title: Inferring Causal Direction from Observational Data: A Complexity Approach
• Authors: Nikolaos Nikolaou and Konstantinos Sechidis
• Abstract summary: We propose several criteria for distinguishing cause and effect in pairs of discrete or continuous random variables. We demonstrate the accuracy of the criteria on synthetic data generated under a broad family of causal mechanisms and types of noise.
• Score: 0.3553493344868413
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: At the heart of causal structure learning from observational data lies a deceivingly simple question: given two statistically dependent random variables, which one has a causal effect on the other? This is impossible to answer using statistical dependence testing alone and requires that we make additional assumptions. We propose several fast and simple criteria for distinguishing cause and effect in pairs of discrete or continuous random variables. The intuition behind them is that predicting the effect variable using the cause variable should be `simpler' than the reverse -- different notions of `simplicity' giving rise to different criteria. We demonstrate the accuracy of the criteria on synthetic data generated under a broad family of causal mechanisms and types of noise.

Related papers

• Score matching through the roof: linear, nonlinear, and latent variables causal discovery [18.46845413928147]
  Causal discovery from observational data holds great promise. Existing methods rely on strong assumptions about the underlying causal structure. We propose a flexible algorithm for causal discovery across linear, nonlinear, and latent variable models.
  arXiv  Detail & Related papers  (2024-07-26T14:09:06Z)
• Nonlinearity, Feedback and Uniform Consistency in Causal Structural Learning [0.8158530638728501]
  Causal Discovery aims to find automated search methods for learning causal structures from observational data. This thesis focuses on two questions in causal discovery: (i) providing an alternative definition of k-Triangle Faithfulness that (i) is weaker than strong faithfulness when applied to the Gaussian family of distributions, and (ii) under the assumption that the modified version of Strong Faithfulness holds.
  arXiv  Detail & Related papers  (2023-08-15T01:23:42Z)
• Structural restrictions in local causal discovery: identifying direct causes of a target variable [0.9208007322096533]
  Learning a set of direct causes of a target variable from an observational joint distribution is a fundamental problem in science. Here, we are only interested in identifying the direct causes of one target variable, not the full DAG. This allows us to relax the identifiability assumptions and develop possibly faster and more robust algorithms.
  arXiv  Detail & Related papers  (2023-07-29T18:31:35Z)
• A Causal Framework for Decomposing Spurious Variations [68.12191782657437]
  We develop tools for decomposing spurious variations in Markovian and Semi-Markovian models. We prove the first results that allow a non-parametric decomposition of spurious effects. The described approach has several applications, ranging from explainable and fair AI to questions in epidemiology and medicine.
  arXiv  Detail & Related papers  (2023-06-08T09:40:28Z)
• Nonparametric Identifiability of Causal Representations from Unknown Interventions [63.1354734978244]
  We study causal representation learning, the task of inferring latent causal variables and their causal relations from mixtures of the variables. Our goal is to identify both the ground truth latents and their causal graph up to a set of ambiguities which we show to be irresolvable from interventional data.
  arXiv  Detail & Related papers  (2023-06-01T10:51:58Z)
• Reinterpreting causal discovery as the task of predicting unobserved joint statistics [15.088547731564782]
  We argue that causal discovery can help inferring properties of the unobserved joint distributions' We define a learning scenario where the input is a subset of variables and the label is some statistical property of that subset.
  arXiv  Detail & Related papers  (2023-05-11T15:30:54Z)
• Typing assumptions improve identification in causal discovery [123.06886784834471]
  Causal discovery from observational data is a challenging task to which an exact solution cannot always be identified. We propose a new set of assumptions that constrain possible causal relationships based on the nature of the variables.
  arXiv  Detail & Related papers  (2021-07-22T14:23:08Z)
• Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests [87.60900567941428]
  A spurious correlation' is the dependence of a model on some aspect of the input data that an analyst thinks shouldn't matter. In machine learning, these have a know-it-when-you-see-it character. We study stress testing using the tools of causal inference.
  arXiv  Detail & Related papers  (2021-05-31T14:39:38Z)
• Latent Instrumental Variables as Priors in Causal Inference based on Independence of Cause and Mechanism [2.28438857884398]
  We study the role of latent variables such as latent instrumental variables and hidden common causes in the causal graphical structures. We derive a novel algorithm to infer causal relationships between two variables.
  arXiv  Detail & Related papers  (2020-07-17T08:18:19Z)
• 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)
• CausalVAE: Structured Causal Disentanglement in Variational Autoencoder [52.139696854386976]
  The framework of variational autoencoder (VAE) is commonly used to disentangle independent factors from observations. We propose a new VAE based framework named CausalVAE, which includes a Causal Layer to transform independent factors into causal endogenous ones. Results show that the causal representations learned by CausalVAE are semantically interpretable, and their causal relationship as a Directed Acyclic Graph (DAG) is identified with good accuracy.
  arXiv  Detail & Related papers  (2020-04-18T20:09:34Z)

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.