Search Results for author:
Found 4 papers, 0 papers with code
An Automated Approach to Causal Inference in Discrete Settings
When causal quantities cannot be point identified, researchers often pursue partial identification to quantify the range of possible values.
Entropic Inequality Constraints from $e$-separation Relations in Directed Acyclic Graphs with Hidden Variables
Directed acyclic graphs (DAGs) with hidden variables are often used to characterize causal relations between variables in a system.
Partial Identifiability in Discrete Data With Measurement Error
When data contains measurement errors, it is necessary to make assumptions relating the observed, erroneous data to the unobserved true phenomena of interest.
Deriving Bounds and Inequality Constraints Using LogicalRelations Among Counterfactuals
We additionally provide inequality constraints on functionals of the observed data law implied by such causal models.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
