1 citations · 1 across the 5 of their papers we have counts for
6 papers
Information-Directed Sampling for Causal Bandits
Muhammad Qasim Elahi, Murat Kocaoglu, Mahsa Ghasemi
Causal bandits exploit structural relationships among variables to share information across interventions and accelerate the identification of high-reward decisions. In many applic…
Characterization and Learning of Causal Graphs from Hard Interventions
Zihan Zhou, Muhammad Qasim Elahi, Murat Kocaoglu
A fundamental challenge in the empirical sciences involves uncovering causal structure through observation and experimentation. Causal discovery entails linking the conditional ind…
Partial Structure Discovery is Sufficient for No-regret Learning in Causal Bandits
Muhammad Qasim Elahi, Mahsa Ghasemi, Murat Kocaoglu
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…
Sample Efficient Bayesian Learning of Causal Graphs from Interventions
Zihan Zhou, Muhammad Qasim Elahi, Murat Kocaoglu
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 o…
Identification of Average Causal Effects in Confounded Additive Noise Models
Muhammad Qasim Elahi, Mahsa Ghasemi, Murat Kocaoglu
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 confo…
Adaptive Online Experimental Design for Causal Discovery
Muhammad Qasim Elahi, Lai Wei, Murat Kocaoglu +1
Causal discovery aims to uncover cause-and-effect relationships encoded in causal graphs by leveraging observational, interventional data, or their combination. The majority of exi…