Search Results for author:
Found 6 papers, 3 papers with code
Score-based Causal Representation Learning: Linear and General Transformations
The paper addresses both the identifiability and achievability aspects.
Robust Causal Bandits for Linear Models
Cumulative regret is adopted as the design criteria, based on which the objective is to design a sequence of interventions that incur the smallest cumulative regret with respect to an oracle aware of the entire causal model and its fluctuations.
General Identifiability and Achievability for Causal Representation Learning
For identifiability, the paper establishes that perfect recovery of the latent causal model and variables is guaranteed under uncoupled interventions.
Score-based Causal Representation Learning with Interventions
The objectives are: (i) recovering the unknown linear transformation (up to scaling) and (ii) determining the directed acyclic graph (DAG) underlying the latent variables.
Causal Bandits for Linear Structural Equation Models
Two linear mechanisms, one soft intervention and one observational, are assumed for each node, giving rise to $2^N$ possible interventions.
Scalable Intervention Target Estimation in Linear Models
This paper considers the problem of estimating the unknown intervention targets in a causal directed acyclic graph from observational and interventional data.
