no code implementations • 15 Jan 2024 • Fateme Jamshidi, Jalal Etesami, Negar Kiyavash
This algorithm generalizes the state-of-the-art methods by allowing non-uniform costs and hidden confounders in the causal graph.
no code implementations • 27 Dec 2023 • Jalal Etesami, Ali Habibnia, Negar Kiyavash
We propose a nonparametric and time-varying directed information graph (TV-DIG) framework to estimate the evolving causal structure in time series networks, thereby addressing the limitations of traditional econometric models in capturing high-dimensional, nonlinear, and time-varying interconnections among series.
no code implementations • 19 Jun 2023 • Yaroslav Kivva, Jalal Etesami, Negar Kiyavash
It extends the results of [Lee et al., 2019, Kivva et al., 2022] on general identifiability (gID) which studied the problem for unconditional causal effects and Shpitser and Pearl [2006b] on identifiability of conditional causal effects given merely the observational distribution $P(\mathbf{V})$ as our algorithm generalizes the algorithms proposed in [Kivva et al., 2022] and [Shpitser and Pearl, 2006b].
1 code implementation • 26 Jan 2023 • Mikhail Konobeev, Jalal Etesami, Negar Kiyavash
We study the causal bandit problem when the causal graph is unknown and develop an efficient algorithm for finding the parent node of the reward node using atomic interventions.
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.
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.
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.
no code implementations • 25 May 2020 • Jalal Etesami, Christoph-Nikolas Straehle
This leads to a set of coupled Bellman equations that describes the behavior of the agents.
no code implementations • 2 Mar 2020 • Jalal Etesami, Philipp Geiger
Learning from demonstrations (LfD) is an efficient paradigm to train AI agents.
no code implementations • 19 Jun 2019 • Huang Xiao, Michael Herman, Joerg Wagner, Sebastian Ziesche, Jalal Etesami, Thai Hong Linh
Imitation Learning describes the problem of recovering an expert policy from demonstrations.
no code implementations • 25 Jan 2018 • Yingxiang Yang, Jalal Etesami, Niao He, Negar Kiyavash
In this paper, we design a nonparametric online algorithm for estimating the triggering functions of multivariate Hawkes processes.
no code implementations • NeurIPS 2017 • Yingxiang Yang, Jalal Etesami, Niao He, Negar Kiyavash
We develop a nonparametric and online learning algorithm that estimates the triggering functions of a multivariate Hawkes process (MHP).
no code implementations • 31 Mar 2017 • Jalal Etesami, Kun Zhang, Negar Kiyavash
Measuring conditional dependencies among the variables of a network is of great interest to many disciplines.
no code implementations • 27 Feb 2017 • Saber Salehkaleybar, Jalal Etesami, Negar Kiyavash, Kun Zhang
We show that the support of transition matrix among the observed processes and lengths of all latent paths between any two observed processes can be identified successfully under some conditions on the VAR model.
no code implementations • 23 Jan 2017 • Saber Salehkaleybar, Jalal Etesami, Negar Kiyavash
We propose an approach for learning the causal structure in stochastic dynamical systems with a $1$-step functional dependency in the presence of latent variables.
no code implementations • 14 Mar 2016 • Jalal Etesami, Negar Kiyavash, Kun Zhang, Kushagra Singhal
This paper studies the problem of recovering the causal structure in network of multivariate linear Hawkes processes.
no code implementations • 22 Sep 2015 • Yingxiang Yang, Jalal Etesami, Negar Kiyavash
This paper addresses the problem of neighborhood selection for Gaussian graphical models.