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  2. Causal Inference Working Group

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Causal Inference in Cheese Production

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The meetings of the Causal Inference Working Group serve as a platform for collaboration and discussion, focussing on causal data analysis in academia and industry. Using causal techniques, the group aims to develop intelligent strategies for data analysis that surpass the correlation based approach of classical statistics, in order to distill robust causal information from

On (Un)observed Confounders in Causal Models

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The meetings of the Causal Inference Working Group serve as a platform for collaboration and discussion, focussing on causal data analysis in academia and industry. Using causal techniques, the group aims to develop intelligent strategies for data analysis that surpass the correlation based approach of classical statistics, in order to distill robust causal information from

End-to-End Causal Analysis in Python with cause2e

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The meetings of the Causal Inference Working Group serve as a platform for collaboration and discussion, focussing on causal data analysis in academia and industry. Using causal techniques, the group aims to develop intelligent strategies for data analysis that surpass the correlation based approach of classical statistics, in order to distill robust causal information from

Discovering Instantaneous Granger Causalities in Non-stationary Categorical Time Series Data. Application to auditory category learning by trial and error.

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The meetings of the Causal Inference Working Group serve as a platform for collaboration and discussion, focussing on causal data analysis in academia and industry. Using causal techniques, the group aims to develop intelligent strategies for data analysis that surpass the correlation based approach of classical statistics, in order to distill robust causal information from