Linear causal disentanglement via interventions
Causal disentanglement seeks a representation of data involving latent variables that are
related via a causal model. A representation is identifiable if both the latent model and the …
related via a causal model. A representation is identifiable if both the latent model and the …
Learning linear causal representations from interventions under general nonlinear mixing
S Buchholz, G Rajendran… - Advances in …, 2024 - proceedings.neurips.cc
We study the problem of learning causal representations from unknown, latent interventions
in a general setting, where the latent distribution is Gaussian but the mixing function is …
in a general setting, where the latent distribution is Gaussian but the mixing function is …
Root cause analysis of failures in microservices through causal discovery
Most cloud applications use a large number of smaller sub-components (called
microservices) that interact with each other in the form of a complex graph to provide the …
microservices) that interact with each other in the form of a complex graph to provide the …
Causal bandits for linear structural equation models
This paper studies the problem of designing an optimal sequence of interventions in a
causal graphical model to minimize cumulative regret with respect to the best intervention in …
causal graphical model to minimize cumulative regret with respect to the best intervention in …
iSCAN: identifying causal mechanism shifts among nonlinear additive noise models
Structural causal models (SCMs) are widely used in various disciplines to represent causal
relationships among variables in complex systems. Unfortunately, the underlying causal …
relationships among variables in complex systems. Unfortunately, the underlying causal …
Learning Unknown Intervention Targets in Structural Causal Models from Heterogeneous Data
Y Yang, S Salehkaleybar… - … Conference on Artificial …, 2024 - proceedings.mlr.press
We study the problem of identifying the unknown intervention targets in structural causal
models where we have access to heterogeneous data collected from multiple environments …
models where we have access to heterogeneous data collected from multiple environments …
Privacy Amplification by Iteration for ADMM with (Strongly) Convex Objective Functions
We study a private variant of ADMM with (strongly) convex objective functions. We consider
a privacy model in which each iteration corresponds to a user whose private function is …
a privacy model in which each iteration corresponds to a user whose private function is …
Separability Analysis for Causal Discovery in Mixture of DAGs
Directed acyclic graphs (DAGs) are effective for compactly representing causal systems and
specifying the causal relationships among the system's constituents. Specifying such causal …
specifying the causal relationships among the system's constituents. Specifying such causal …
Intervention target estimation in the presence of latent variables
This paper considers the problem of estimating unknown intervention targets in causal
directed acyclic graphs from observational and interventional data in the presence of latent …
directed acyclic graphs from observational and interventional data in the presence of latent …
Root cause discovery via permutations and Cholesky decomposition
This work is motivated by the following problem: Can we identify the disease-causing gene
in a patient affected by a monogenic disorder? This problem is an instance of root cause …
in a patient affected by a monogenic disorder? This problem is an instance of root cause …