Zobrazeno 1 - 10
of 59
pro vyhledávání: '"Etesami, Jalal"'
Identifying causal effects is a key problem of interest across many disciplines. The two long-standing approaches to estimate causal effects are observational and experimental (randomized) studies. Observational studies can suffer from unmeasured con
Externí odkaz:
http://arxiv.org/abs/2407.05330
Linear non-Gaussian causal models postulate that each random variable is a linear function of parent variables and non-Gaussian exogenous error terms. We study identification of the linear coefficients when such models contain latent variables. Our f
Externí odkaz:
http://arxiv.org/abs/2405.20856
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-dimens
Externí odkaz:
http://arxiv.org/abs/2312.16707
We address the problem of identifiability of an arbitrary conditional causal effect given both the causal graph and a set of any observational and/or interventional distributions of the form $Q[S]:=P(S|do(V\setminus S))$, where $V$ denotes the set of
Externí odkaz:
http://arxiv.org/abs/2306.11755
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. We derive the exact equation for the expected number of interventions pe
Externí odkaz:
http://arxiv.org/abs/2301.11401
We study the complexity of finding the global solution to stochastic nonconvex optimization when the objective function satisfies global Kurdyka-Lojasiewicz (KL) inequality and the queries from stochastic gradient oracles satisfy mild expected smooth
Externí odkaz:
http://arxiv.org/abs/2210.01748
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. Existing ordering-based methods in the literatu
Externí odkaz:
http://arxiv.org/abs/2208.06935
Autor:
Akbari, Sina, Jamshidi, Fateme, Mokhtarian, Ehsan, Vowels, Matthew J., Etesami, Jalal, Kiyavash, Negar
Causal identification is at the core of the causal inference literature, where complete algorithms have been proposed to identify causal queries of interest. The validity of these algorithms hinges on the restrictive assumption of having access to a
Externí odkaz:
http://arxiv.org/abs/2208.04627
We revisit the problem of general identifiability originally introduced in [Lee et al., 2019] for causal inference and note that it is necessary to add positivity assumption of observational distribution to the original definition of the problem. We
Externí odkaz:
http://arxiv.org/abs/2206.01081
Pearl's do calculus is a complete axiomatic approach to learn the identifiable causal effects from observational data. When such an effect is not identifiable, it is necessary to perform a collection of often costly interventions in the system to lea
Externí odkaz:
http://arxiv.org/abs/2205.02232