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Found 4 papers, 1 papers with code
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.
Leveraging directed causal discovery to detect latent common causes
Here, we devise a general heuristic which takes a causal discovery algorithm that can only distinguish purely directed causal relations and modifies it to also detect latent common causes.
MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming
We elaborate on using importance sampling for causal reasoning, in particular for counterfactual inference.
Causal inference via algebraic geometry: feasibility tests for functional causal structures with two binary observed variables
We provide a scheme for inferring causal relations from uncontrolled statistical data based on tools from computational algebraic geometry, in particular, the computation of Groebner bases.
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