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Causal Discovery using Bayesian Model Selection
With only observational data on two variables, and without other assumptions, it is not possible to infer which one causes the other.
Generalization bounds and algorithms for estimating conditional average treatment effect of dosage
We investigate the task of estimating the conditional average causal effect of treatment-dosage pairs from a combination of observational data and assumptions on the causal relationships in the underlying system.
Integrating overlapping datasets using bivariate causal discovery
Previous approaches to overcoming this shortcoming devised algorithms that returned all joint causal structures consistent with the conditional independence information contained in each individual dataset.
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