A Ladder of Causal Distances

• URL: http://arxiv.org/abs/2005.02480v2
• Date: Wed, 25 Aug 2021 08:53:06 GMT
• Title: A Ladder of Causal Distances
• Authors: Maxime Peyrard and Robert West
• Abstract summary: We introduce a hierarchy of three distances, one for each rung of the "ladder of causation" We put our causal distances to use by benchmarking standard causal discovery systems on both synthetic and real-world datasets. Finally, we highlight the usefulness of our causal distances by briefly discussing further applications beyond the evaluation of causal discovery techniques.
• Score: 44.34185575573054
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Causal discovery, the task of automatically constructing a causal model from data, is of major significance across the sciences. Evaluating the performance of causal discovery algorithms should ideally involve comparing the inferred models to ground-truth models available for benchmark datasets, which in turn requires a notion of distance between causal models. While such distances have been proposed previously, they are limited by focusing on graphical properties of the causal models being compared. Here, we overcome this limitation by defining distances derived from the causal distributions induced by the models, rather than exclusively from their graphical structure. Pearl and Mackenzie (2018) have arranged the properties of causal models in a hierarchy called the "ladder of causation" spanning three rungs: observational, interventional, and counterfactual. Following this organization, we introduce a hierarchy of three distances, one for each rung of the ladder. Our definitions are intuitively appealing as well as efficient to compute approximately. We put our causal distances to use by benchmarking standard causal discovery systems on both synthetic and real-world datasets for which ground-truth causal models are available. Finally, we highlight the usefulness of our causal distances by briefly discussing further applications beyond the evaluation of causal discovery techniques.

Related papers

• Sample, estimate, aggregate: A recipe for causal discovery foundation models [28.116832159265964]
  We train a supervised model that learns to predict a larger causal graph from the outputs of classical causal discovery algorithms run over subsets of variables. Our approach is enabled by the observation that typical errors in the outputs of classical methods remain comparable across datasets. Experiments on real and synthetic data demonstrate that this model maintains high accuracy in the face of misspecification or distribution shift.
  arXiv  Detail & Related papers  (2024-02-02T21:57:58Z)
• Towards Causal Foundation Model: on Duality between Causal Inference and Attention [18.046388712804042]
  We take a first step towards building causally-aware foundation models for treatment effect estimations. We propose a novel, theoretically justified method called Causal Inference with Attention (CInA)
  arXiv  Detail & Related papers  (2023-10-01T22:28:34Z)
• Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning [98.78136504619539]
  Causal Triplet is a causal representation learning benchmark featuring visually more complex scenes. We show that models built with the knowledge of disentangled or object-centric representations significantly outperform their distributed counterparts.
  arXiv  Detail & Related papers  (2023-01-12T17:43:38Z)
• Active Bayesian Causal Inference [72.70593653185078]
  We propose Active Bayesian Causal Inference (ABCI), a fully-Bayesian active learning framework for integrated causal discovery and reasoning. ABCI jointly infers a posterior over causal models and queries of interest. We show that our approach is more data-efficient than several baselines that only focus on learning the full causal graph.
  arXiv  Detail & Related papers  (2022-06-04T22:38:57Z)
• Estimation of Bivariate Structural Causal Models by Variational Gaussian Process Regression Under Likelihoods Parametrised by Normalising Flows [74.85071867225533]
  Causal mechanisms can be described by structural causal models. One major drawback of state-of-the-art artificial intelligence is its lack of explainability.
  arXiv  Detail & Related papers  (2021-09-06T14:52:58Z)
• Context-Specific Causal Discovery for Categorical Data Using Staged Trees [2.9926023796813737]
  Causal discovery algorithms aim at untangling complex causal relationships using observational data only. We introduce new causal discovery algorithms based on staged tree models, which can represent complex and non-symmetric causal effects.
  arXiv  Detail & Related papers  (2021-06-08T14:46:15Z)
• Causal Expectation-Maximisation [70.45873402967297]
  We show that causal inference is NP-hard even in models characterised by polytree-shaped graphs. We introduce the causal EM algorithm to reconstruct the uncertainty about the latent variables from data about categorical manifest variables. We argue that there appears to be an unnoticed limitation to the trending idea that counterfactual bounds can often be computed without knowledge of the structural equations.
  arXiv  Detail & Related papers  (2020-11-04T10:25:13Z)
• Structural Causal Models Are (Solvable by) Credal Networks [70.45873402967297]
  Causal inferences can be obtained by standard algorithms for the updating of credal nets. This contribution should be regarded as a systematic approach to represent structural causal models by credal networks. Experiments show that approximate algorithms for credal networks can immediately be used to do causal inference in real-size problems.
  arXiv  Detail & Related papers  (2020-08-02T11:19:36Z)
• Inferring Individual Level Causal Models from Graph-based Relational Time Series [3.332377849866735]
  We formalize the problem of causal inference over graph-based relational time-series data. We propose causal inference models that leverage both the graph topology and time-series to accurately estimate local causal effects of nodes.
  arXiv  Detail & Related papers  (2020-01-16T18:48:40Z)

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.