Results 31 to 40 of about 1,964,226 (308)
Differentiable Causal Backdoor Discovery
, 2020 Discovering the causal effect of a decision is critical to nearly all forms of decision-making. In particular, it is a key quantity in drug development, in crafting government policy, and when implementing a real-world machine learning system. Given only observational data, confounders often obscure the true causal effect. Luckily, in some cases, it is
Gultchin, L +3 more
openaire +5 more sources
Discovering Ecological Relationships in Flowing Freshwater Ecosystems
Frontiers in Ecology and Evolution, 2022 Knowledge of ecological responses to changes in the environment is vital to design appropriate measures for conserving biodiversity. Experimental studies are the standard to identify ecological cause-effect relationships, but their results do not ...
Konrad P. Mielke +9 more
doaj +1 more source
Scalable Causal Discovery with Score Matching [PDF]
CLEaR, 2023 This paper demonstrates how to discover the whole causal graph from the second derivative of the log-likelihood in observational non-linear additive Gaussian noise models. Leveraging scalable machine learning approaches to approximate the score function $
Francesco Montagna +4 more
semanticscholar +1 more source
Learning latent functions for causal discovery
Machine Learning: Science and Technology, 2023 Causal discovery from observational data offers unique opportunities in many scientific disciplines: reconstructing causal drivers, testing causal hypotheses, and comparing and evaluating models for optimizing targeted interventions.
Emiliano Díaz +3 more
doaj +1 more source
Causal Discovery with Language Models as Imperfect Experts [PDF]
arXiv.org, 2023 Understanding the causal relationships that underlie a system is a fundamental prerequisite to accurate decision-making. In this work, we explore how expert knowledge can be used to improve the data-driven identification of causal graphs, beyond Markov ...
Stephanie Long +4 more
semanticscholar +1 more source
Causal networks for climate model evaluation and constrained projections [PDF]
, 2020 Global climate models are central tools for understanding past and future climate change. The assessment of model skill, in turn, can benefit from modern data science approaches. Here we apply causal discovery algorithms to sea level pressure data from a
Eyring, Veronika +3 more
core +2 more sources
Markov Boundary Discovery with Ridge Regularized Linear Models
Journal of Causal Inference, 2016 Ridge regularized linear models (RRLMs), such as ridge regression and the SVM, are a popular group of methods that are used in conjunction with coefficient hypothesis testing to discover explanatory variables with a significant multivariate association ...
Strobl Eric V., Visweswaran Shyam
doaj +1 more source
Testability of Instrumental Variables in Linear Non-Gaussian Acyclic Causal Models
Entropy, 2022 This paper investigates the problem of selecting instrumental variables relative to a target causal influence X→Y from observational data generated by linear non-Gaussian acyclic causal models in the presence of unmeasured confounders.
Feng Xie +5 more
doaj +1 more source
Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization [PDF]
arXiv.org, 2023 In this paper, we propose REASON, a novel framework that enables the automatic discovery of both intra-level (i.e., within-network) and inter-level (i.e., across-network) causal relationships for root cause localization.
Dongjie Wang +6 more
semanticscholar +1 more source
CausalFormer: An Interpretable Transformer for Temporal Causal Discovery [PDF]
IEEE Transactions on Knowledge and Data EngineeringTemporal causal discovery is a crucial task aimed at uncovering the causal relations within time series data. The latest temporal causal discovery methods usually train deep learning models on prediction tasks to uncover the causality between time series.
Lingbai Kong +5 more
semanticscholar +1 more source