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An introduction to causal discovery
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
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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
CoRR, 2022 It is crucial to consider the social and ethical consequences of AI and ML based decisions for the safe and acceptable use of these emerging technologies. Fairness, in particular, guarantees that the ML decisions do not result in discrimination against individuals or minorities.
Ruta Binkyte-Sadauskiene +4 more
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Causal KL: Evaluating Causal Discovery
CoRR, 2021 26 ...
Rodney T. O'Donnell +2 more
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Semi-supervised Learning of Visual Causal Macrovariables
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2023 Discovery of causally related concepts is one of the key challenges in extracting knowledge from observational data. Lower-dimensional “causal macrovariables” represent concepts which preserve all relevant causal information in high-dimensional systems ...
Aruna Jammalamadaka +6 more
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Causal-learn: Causal Discovery in Python
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 a comprehensive collection of causal discovery methods to both practitioners and researchers.
Yujia Zheng 0001 +8 more
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Causal discovery and epidemiology: a potential for synergy [PDF]
American Journal of EpidemiologyMerete Osler +2 more
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Greedy Causal Discovery Is Geometric
SIAM Journal on Discrete Mathematics, 2023 Finding a directed acyclic graph (DAG) that best encodes the conditional independence statements observable from data is a central question within causality. Algorithms that greedily transform one candidate DAG into another given a fixed set of moves have been particularly successful, for example the GES, GIES, and MMHC algorithms.
Svante Linusson +2 more
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Optimal transport for causal discovery
CoRR, 2022 To determine causal relationships between two variables, approaches based on Functional Causal Models (FCMs) have been proposed by properly restricting model classes; however, the performance is sensitive to the model assumptions, which makes it difficult to use.
Tu, Ruibo +3 more
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, 2022 Causal discovery for purely observational, categorical data is a long-standing challenging problem. Unlike continuous data, the vast majority of existing methods for categorical data focus on inferring the Markov equivalence class only, which leaves the direction of some causal relationships undetermined.
Yang Ni, Bani K. Mallick
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