Zobrazeno 1 - 10
of 271
pro vyhledávání: '"Kiyavash, Negar"'
This work investigates the performance limits of projected stochastic first-order methods for minimizing functions under the $(\alpha,\tau,\mathcal{X})$-projected-gradient-dominance property, that asserts the sub-optimality gap $F(\mathbf{x})-\min_{\
Externí odkaz:
http://arxiv.org/abs/2408.01839
The presence of unobserved common causes and the presence of measurement error are two of the most limiting challenges in the task of causal structure learning. Ignoring either of the two challenges can lead to detecting spurious causal links among v
Externí odkaz:
http://arxiv.org/abs/2407.19426
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
We study the generic identifiability of causal effects in linear non-Gaussian acyclic models (LiNGAM) with latent variables. We consider the problem in two main settings: When the causal graph is known a priori, and when it is unknown. In both settin
Externí odkaz:
http://arxiv.org/abs/2406.02049
The s-ID problem seeks to compute a causal effect in a specific sub-population from the observational data pertaining to the same sub population (Abouei et al., 2023). This problem has been addressed when all the variables in the system are observabl
Externí odkaz:
http://arxiv.org/abs/2405.14547
Causal discovery, i.e., learning the causal graph from data, is often the first step toward the identification and estimation of causal effects, a key requirement in numerous scientific domains. Causal discovery is hampered by two main challenges: li
Externí odkaz:
http://arxiv.org/abs/2403.09300
Autor:
Akbari, Sina, Kiyavash, Negar
The renowned difference-in-differences (DiD) estimator relies on the assumption of 'parallel trends,' which does not hold in many practical applications. To address this issue, the econometrics literature has turned to the triple difference estimator
Externí odkaz:
http://arxiv.org/abs/2402.12583
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 study the problem of identifying the unknown intervention targets in structural causal models where we have access to heterogeneous data collected from multiple environments. The unknown intervention targets are the set of endogenous variables who
Externí odkaz:
http://arxiv.org/abs/2312.06091
Policy gradient (PG) is widely used in reinforcement learning due to its scalability and good performance. In recent years, several variance-reduced PG methods have been proposed with a theoretical guarantee of converging to an approximate first-orde
Externí odkaz:
http://arxiv.org/abs/2311.08914