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A Meta-Learning Approach to Bayesian Causal Discovery
To address these limitations, we propose a Bayesian meta learning model that allows for sampling causal structures from the posterior and encodes these key properties.
Continuous Bayesian Model Selection for Multivariate Causal Discovery
Current causal discovery approaches require restrictive model assumptions or assume access to interventional data to ensure structure identifiability.
Bivariate Causal Discovery using Bayesian Model Selection
Identifying causal direction then becomes a Bayesian model selection problem.
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.
