Results 11 to 20 of about 1,915,617 (250)

Discovering cyclic causal models in psychological research [PDF]

advances.in/psychology
Statistical network models have become popular tools for analyzing multivariate psychological data. In empirical practice, network parameters are often interpreted as reflecting causal relationships – an approach that can be characterized as a form of ...
Kyuri Park, Lourens J. Waldorp, Oisín Ryan   +2 more
doaj   +3 more sources

An introduction to causal discovery [PDF]

Swiss Journal of Economics and Statistics
In 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   +3 more sources

The KDD 2021 Workshop on Causal Discovery (CD2021)

, 2021
As a basic and effective tool for explanation, prediction and decision making, causal relationships have been utilized in almost all disciplines. Traditionally, causal relationships are identified by making use of interventions or randomized controlled ...
Cooper, Gregory, Bareinboim, Elias, Li, Jiuyong, Liu, Huan, Thuc Duy Le, Triantafyllou, Sofia, Kiyavash, Negar, Cooper, G., Huan Liu, Sofia Triantafyllou, Elias Bareinboim, Negar Kiyavash, Gregory Cooper, Kiyavash, N., Jiuyong Li, Li, J., Liu, H., Triantafyllou, S., Le, T.D., Bareinboim, E.   +19 more
core   +2 more sources

Causal-learn: Causal Discovery in Python [PDF]

, 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
Chen, Wei, Huang, Biwei, Zheng, Yujia, Cai, Ruichu, Ramsey, Joseph, Zhang, Kun, Gong, Mingming, Shimizu, Shohei, Spirtes, Peter   +8 more
core   +1 more source

Simple low cost causal discovery using mutual information and domain knowledge [PDF]

, 2011
PhDThis thesis examines causal discovery within datasets, in particular observational datasets where normal experimental manipulation is not possible. A number of machine learning techniques are examined in relation to their use of knowledge and the ...
Joseph, Adrian
core   +2 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, Konrad P. Mielke, Aafke M. Schipper, Aafke M. Schipper, Tom Heskes, Michiel C. Zijp, Leo Posthuma, Leo Posthuma, Mark A. J. Huijbregts, Tom Claassen   +9 more
doaj   +1 more source

Mining Causality via Information Bottleneck [PDF]

Jisuanji kexue, 2022
Causal discovery from observational data is a fundamental problem in many disciplines.However,existing methods such as constraint-based methods and causal function-based methods have strong assumptions on the causal mechanism of data,and are only ...
QIAO Jie, CAI Rui-chu, HAO Zhi-feng
doaj   +1 more source

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

The KDD 2022 Workshop on Causal Discovery (CD2022)

, 2022
Causal relationships have been utilized in almost all disciplines, and the research into causal discovery has attracted a lot of attention in the last few years.
Emre Kiciman, Kiciman, E., Huan Liu, Liu, L., Liu, H., Triantafyllou, S., Thuc Duy Le, Le, T.D., Lin Liu, Sofia Triantafyllou   +9 more
core   +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, Yangbo He, Zhi Geng, Zhengming Chen, Ru Hou, Kun Zhang   +5 more
doaj   +1 more source