Zobrazeno 1 - 10
of 210
pro vyhledávání: '"Kocaoğlu, Murat"'
Causal knowledge about the relationships among decision variables and a reward variable in a bandit setting can accelerate the learning of an optimal decision. Current works often assume the causal graph is known, which may not always be available a
Externí odkaz:
http://arxiv.org/abs/2411.04054
Causal discovery is a fundamental problem with applications spanning various areas in science and engineering. It is well understood that solely using observational data, one can only orient the causal graph up to its Markov equivalence class, necess
Externí odkaz:
http://arxiv.org/abs/2410.20089
In high-stake domains such as healthcare and hiring, the role of machine learning (ML) in decision-making raises significant fairness concerns. This work focuses on Counterfactual Fairness (CF), which posits that an ML model's outcome on any individu
Externí odkaz:
http://arxiv.org/abs/2409.01977
Additive noise models (ANMs) are an important setting studied in causal inference. Most of the existing works on ANMs assume causal sufficiency, i.e., there are no unobserved confounders. This paper focuses on confounded ANMs, where a set of treatmen
Externí odkaz:
http://arxiv.org/abs/2407.10014
Discovering causal relations from observational time series without making the stationary assumption is a significant challenge. In practice, this challenge is common in many areas, such as retail sales, transportation systems, and medical science. H
Externí odkaz:
http://arxiv.org/abs/2407.07291
Change point detection in time series seeks to identify times when the probability distribution of time series changes. It is widely applied in many areas, such as human-activity sensing and medical science. In the context of multivariate time series
Externí odkaz:
http://arxiv.org/abs/2407.07290
Causal discovery aims to uncover cause-and-effect relationships encoded in causal graphs by leveraging observational, interventional data, or their combination. The majority of existing causal discovery methods are developed assuming infinite interve
Externí odkaz:
http://arxiv.org/abs/2405.11548
Causal inference from observational data plays critical role in many applications in trustworthy machine learning. While sound and complete algorithms exist to compute causal effects, many of them assume access to conditional likelihoods, which is di
Externí odkaz:
http://arxiv.org/abs/2402.07419
Autor:
Rahman, Md Musfiqur, Kocaoglu, Murat
Sound and complete algorithms have been proposed to compute identifiable causal queries using the causal structure and data. However, most of these algorithms assume accurate estimation of the data distribution, which is impractical for high-dimensio
Externí odkaz:
http://arxiv.org/abs/2401.01426
Causal effect estimation has been studied by many researchers when only observational data is available. Sound and complete algorithms have been developed for pointwise estimation of identifiable causal queries. For non-identifiable causal queries, r
Externí odkaz:
http://arxiv.org/abs/2306.13242