1 code implementation • 14 Mar 2024 • Ehsan Mokhtarian, Sepehr Elahi, Sina Akbari, Negar Kiyavash
Presence and identification of removable variables allow recursive approaches for causal discovery, a promising solution that helps to address the aforementioned challenges by reducing the problem size successively.
no code implementations • 26 May 2023 • Sina Akbari, Luca Ganassali
We study the problem of causal structure learning from data using optimal transport (OT).
no code implementations • 4 May 2022 • Sina Akbari, Jalal Etesami, Negar Kiyavash
When such an effect is not identifiable, it is necessary to perform a collection of often costly interventions in the system to learn the causal effect.
no code implementations • 18 Feb 2022 • Matthew J. Vowels, Sina Akbari, Necati Cihan Camgoz, Richard Bowden
Unfortunately, they are unlikely to be sufficiently flexible to be able to adequately model real-world phenomena, and may yield biased estimates.
1 code implementation • NeurIPS 2021 • Sina Akbari, Ehsan Mokhtarian, AmirEmad Ghassami, Negar Kiyavash
The upper bound of our proposed approach and the lower bound at most differ by a factor equal to the number of variables in the worst case.
1 code implementation • 10 Oct 2020 • Ehsan Mokhtarian, Sina Akbari, AmirEmad Ghassami, Negar Kiyavash
In this paper, we propose a novel recursive constraint-based method for causal structure learning that significantly reduces the required number of CI tests compared to the existing literature.