Results 1 to 10 of about 1,221,472 (206)
Assimilative causal inference [PDF]
Nature CommunicationsCausal inference is fundamental across scientific disciplines, yet existing methods struggle to capture instantaneous, time-evolving causal relationships in complex, high-dimensional systems.
Marios Andreou, Nan Chen, Erik Bollt
doaj +6 more sources
Engineering, 2020 Causal inference is a powerful modeling tool for explanatory analysis, which might enable current machine learning to become explainable. How to marry causal inference with machine learning to develop explainable artificial intelligence (XAI) algorithms ...
Kun Kuang +9 more
doaj +3 more sources
A survey on causal inference for recommendation [PDF]
The Innovation, 2023 Causal inference has recently garnered significant interest among recommender system (RS) researchers due to its ability to dissect cause-and-effect relationships and its broad applicability across multiple fields.
Huishi Luo +4 more
semanticscholar +5 more sources
A Survey on Causal Inference [PDF]
ACM Transactions on Knowledge Discovery from Data, 2020 Causal inference is a critical research topic across many domains, such as statistics, computer science, education, public policy, and economics, for decades.
Liuyi Yao +5 more
semanticscholar +3 more sources
International Journal of Public Health, 2023 Objectives: The aging of the South African population could have profound implications for the independence and overall quality of life of older adults as life expectancy increases. While there is evidence that lifetime socio-economic status shapes risks
Keletso Makofane +4 more
doaj +1 more source
CoRR, 2022 Estimating an individual's potential outcomes under counterfactual treatments is a challenging task for traditional causal inference and supervised learning approaches when the outcome is high-dimensional (e.g. gene expressions, impulse responses, human faces) and covariates are relatively limited.
Wu, Yulun +5 more
openaire +2 more sources
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond [PDF]
Transactions of the Association for Computational Linguistics, 2021 A fundamental goal of scientific research is to learn about causal relationships. However, despite its critical role in the life and social sciences, causality has not had the same importance in Natural Language Processing (NLP), which has traditionally ...
Amir Feder +12 more
semanticscholar +1 more source
Matching methods for causal inference: A review and a look forward [PDF]
Statistical Science, 2010 When estimating causal effects using observational data, it is desirable to replicate a randomized experiment as closely as possible by obtaining treated and control groups with similar covariate distributions. This goal can often be achieved by choosing
E. Stuart
semanticscholar +1 more source
On the dimensional indeterminacy of one-wave factor analysis under causal effects
Journal of Causal Inference, 2023 It is shown, with two sets of indicators that separately load on two distinct factors, independent of one another conditional on the past, that if it is the case that at least one of the factors causally affects the other, then, in many settings, the ...
VanderWeele Tyler J. +1 more
doaj +1 more source
Simple yet sharp sensitivity analysis for unmeasured confounding
Journal of Causal Inference, 2022 We present a method for assessing the sensitivity of the true causal effect to unmeasured confounding. The method requires the analyst to set two intuitive parameters. Otherwise, the method is assumption free. The method returns an interval that contains
Peña Jose M.
doaj +1 more source