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pro vyhledávání: '"Karavani, Ehud"'
Interpretability and transparency are essential for incorporating causal effect models from observational data into policy decision-making. They can provide trust for the model in the absence of ground truth labels to evaluate the accuracy of such mo
Externí odkaz:
http://arxiv.org/abs/2401.17737
Causal inference is a statistical paradigm for quantifying causal effects using observational data. It is a complex process, requiring multiple steps, iterations, and collaborations with domain experts. Analysts often rely on visualizations to evalua
Externí odkaz:
http://arxiv.org/abs/2303.00617
Theoretical guarantees for causal inference using propensity scores are partly based on the scores behaving like conditional probabilities. However, scores between zero and one, especially when outputted by flexible statistical estimators, do not nec
Externí odkaz:
http://arxiv.org/abs/2211.01221
The assumption of positivity in causal inference (also known as common support and co-variate overlap) is necessary to obtain valid causal estimates. Therefore, confirming it holds in a given dataset is an important first step of any causal analysis.
Externí odkaz:
http://arxiv.org/abs/1907.08127
Autor:
Shimoni, Yishai, Karavani, Ehud, Ravid, Sivan, Bak, Peter, Ng, Tan Hung, Alford, Sharon Hensley, Meade, Denise, Goldschmidt, Yaara
Real world observational data, together with causal inference, allow the estimation of causal effects when randomized controlled trials are not available. To be accepted into practice, such predictive models must be validated for the dataset at hand,
Externí odkaz:
http://arxiv.org/abs/1906.00442
Akademický článek
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Causal inference analysis is the estimation of the effects of actions on outcomes. In the context of healthcare data this means estimating the outcome of counter-factual treatments (i.e. including treatments that were not observed) on a patient's out
Externí odkaz:
http://arxiv.org/abs/1802.05046
Publikováno v:
Epidemiology; Jul2024, Vol. 35 Issue 4, p473-480, 8p
Publikováno v:
Statistical Science, 2019 Feb 01. 34(1), 86-89.
Externí odkaz:
https://www.jstor.org/stable/26771036