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Methods and tools for causal discovery and causal inference [PDF]
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2022 Causality is a complex concept, which roots its developments across several fields, such as statistics, economics, epidemiology, computer science, and philosophy.
Ana Rita Nogueira +2 more
exaly +8 more sources
An introduction to causal discovery [PDF]
Swiss Journal of Economics and StatisticsIn social sciences and economics, causal inference traditionally focuses on assessing the impact of predefined treatments (or interventions) on predefined outcomes, such as the effect of education programs on earnings. Causal discovery, in contrast, aims
Martin Huber
doaj +5 more sources
Causal-learn: Causal Discovery in Python [PDF]
J. Mach. Learn. Res., 2023 Causal discovery aims at revealing causal relations from observational data, which is a fundamental task in science and engineering. We describe $\textit{causal-learn}$, an open-source Python library for causal discovery. This library focuses on bringing
Yujia Zheng +8 more
semanticscholar +5 more sources
Review of Causal Discovery Methods Based on Graphical Models
Frontiers in Genetics, 2019 A fundamental task in various disciplines of science, including biology, is to find underlying causal relations and make use of them. Causal relations can be seen if interventions are properly applied; however, in many cases they are difficult or even ...
Clark Glymour, Kun Zhang, Peter Spirtes
doaj +4 more sources
Argumentative Causal Discovery
Proceedings of the TwentyFirst International Conference on Principles of Knowledge Representation and ReasoningCausal discovery amounts to unearthing causal relationships amongst features in data. It is a crucial companion to causal inference, necessary to build scientific knowledge without resorting to expensive or impossible randomised control trials. In this
Fabrizio Russo +2 more
semanticscholar +6 more sources
A spatiotemporal stochastic climate model for benchmarking causal discovery methods for teleconnections [PDF]
Environmental Data Science, 2022 Teleconnections that link climate processes at widely separated spatial locations form a key component of the climate system. Their analysis has traditionally been based on means, climatologies, correlations, or spectral properties, which cannot always ...
Xavier-Andoni Tibau +5 more
doaj +2 more sources
Applying causal discovery to single-cell analyses using CausalCell [PDF]
eLife, 2023 Correlation between objects is prone to occur coincidentally, and exploring correlation or association in most situations does not answer scientific questions rich in causality.
Yujian Wen +7 more
doaj +2 more sources
Power Analysis for Causal Discovery. [PDF]
Int J Data Sci Anal, 2022 Abstract Causal discovery algorithms have the potential to impact many fields of science. However, substantial foundational work on the statistical properties of causal discovery algorithms is still needed. This paper presents what is to our knowledge the first method for conducting power analysis for causal discovery algorithms.
Kummerfeld E, Williams L, Ma S.
europepmc +3 more sources
Causal discovery for the microbiome. [PDF]
Lancet Microbe, 2022 Measurement and manipulation of the microbiome is generally considered to have great potential for understanding the causes of complex diseases in humans, developing new therapies, and finding preventive measures. Many studies have found significant associations between the microbiome and various diseases; however, Koch's classical postulates remind us
Corander J, Hanage WP, Pensar J.
europepmc +4 more sources
Bayesian Networks and Causal Discovery [PDF]
EntropyThe discovery of the precise causal representations underlying complex data forms the bedrock of artificial intelligence research [...]
Xiaoguang Gao, Zidong Wang
doaj +2 more sources