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.
1 code implementation • 5 Nov 2023 • Ishan Kumar, Zhijing Jin, Ehsan Mokhtarian, Siyuan Guo, Yuen Chen, Mrinmaya Sachan, Bernhard Schölkopf
Evaluating the significance of a paper is pivotal yet challenging for the scientific community.
1 code implementation • 5 Sep 2023 • Amir Mohammad Abouei, Ehsan Mokhtarian, Negar Kiyavash
We introduce and advocate for a causal inference problem in sub-populations (henceforth called s-ID), in which we merely have access to observational data of the targeted sub-population (as opposed to the entire population).
no code implementations • 14 Aug 2022 • Ehsan Mokhtarian, Mohammadsadegh Khorasani, Jalal Etesami, Negar Kiyavash
We propose ordering-based approaches for learning the maximal ancestral graph (MAG) of a structural equation model (SEM) up to its Markov equivalence class (MEC) in the presence of unobserved variables.
no code implementations • 2 Jun 2022 • Yaroslav Kivva, Ehsan Mokhtarian, Jalal Etesami, Negar Kiyavash
A nice property of this new algorithm is that it establishes a connection between general identifiability and classical identifiability by Pearl [1995] through decomposing the general identifiability problem into a series of classical identifiability sub-problems.
1 code implementation • 20 May 2022 • Ehsan Mokhtarian, Saber Salehkaleybar, AmirEmad Ghassami, Negar Kiyavash
We study experiment design for unique identification of the causal graph of a simple SCM, where the graph may contain cycles.
1 code implementation • 22 Oct 2021 • Ehsan Mokhtarian, Fateme Jamshidi, Jalal Etesami, Negar Kiyavash
We study the problem of causal effect identification from observational distribution given the causal graph and some context-specific independence (CSI) relations.
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.