no code implementations • 1 Feb 2024 • Burak Varici, Emre Acartürk, Karthikeyan Shanmugam, Abhishek Kumar, Ali Tajer
The paper addresses both the identifiability and achievability aspects.
no code implementations • 30 Oct 2023 • Zirui Yan, Arpan Mukherjee, Burak Varici, Ali Tajer
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.
1 code implementation • 24 Oct 2023 • Burak Varici, Emre Acartürk, Karthikeyan Shanmugam, Ali Tajer
For identifiability, the paper establishes that perfect recovery of the latent causal model and variables is guaranteed under uncoupled interventions.
no code implementations • 19 Jan 2023 • Burak Varici, Emre Acarturk, Karthikeyan Shanmugam, Abhishek Kumar, Ali Tajer
The objectives are: (i) recovering the unknown linear transformation (up to scaling) and (ii) determining the directed acyclic graph (DAG) underlying the latent variables.
1 code implementation • 26 Aug 2022 • Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer
Two linear mechanisms, one soft intervention and one observational, are assumed for each node, giving rise to $2^N$ possible interventions.
1 code implementation • NeurIPS 2021 • Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer
This paper considers the problem of estimating the unknown intervention targets in a causal directed acyclic graph from observational and interventional data.