1 code implementation • 26 Oct 2023 • Bryan Andrews, Joseph Ramsey, Ruben Sanchez-Romero, Jazmin Camchong, Erich Kummerfeld
However, the accuracy and execution time of learning algorithms generally struggle to scale to problems with hundreds of highly connected variables -- for instance, recovering brain networks from fMRI data.
1 code implementation • 31 Jul 2023 • Yujia Zheng, Biwei Huang, Wei Chen, Joseph Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang
Causal discovery aims at revealing causal relations from observational data, which is a fundamental task in science and engineering.
1 code implementation • 11 Jun 2022 • Wai-Yin Lam, Bryan Andrews, Joseph Ramsey
There has been an increasing interest in methods that exploit permutation reasoning to search for directed acyclic causal models, including the "Ordering Search" of Teyssier and Kohler and GSP of Solus, Wang and Uhler.
1 code implementation • 25 Feb 2022 • Anne Helby Petersen, Joseph Ramsey, Claus Thorn Ekstrøm, Peter Spirtes
We use random subsampling to investigate real data performance on small samples and again find that SLdisco is less sensitive towards sample size and hence seems to better utilize the information available in small datasets.
no code implementations • 26 Mar 2021 • Wei Chen, Kun Zhang, Ruichu Cai, Biwei Huang, Joseph Ramsey, Zhifeng Hao, Clark Glymour
The first step of our method uses the FCI procedure, which allows confounders and is able to produce asymptotically correct results.
no code implementations • 5 Mar 2019 • Biwei Huang, Kun Zhang, Jiji Zhang, Joseph Ramsey, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf
In this paper, we develop a framework for causal discovery from such data, called Constraint-based causal Discovery from heterogeneous/NOnstationary Data (CD-NOD), to find causal skeleton and directions and estimate the properties of mechanism changes.
no code implementations • 27 Jan 2019 • Biwei Huang, Kun Zhang, Ruben Sanchez-Romero, Joseph Ramsey, Madelyn Glymour, Clark Glymour
A substantial body of researches use Pearson's correlation coefficients, mutual information, or partial correlation to investigate the differences in brain connectivities between ASD and typical controls from functional Magnetic Resonance Imaging (fMRI).
no code implementations • 6 May 2018 • Joseph Ramsey, Bryan Andrews
We report a procedure that, in one step from continuous data with minimal preparation, recovers the graph found by Sachs et al. \cite{sachs2005causal}, with only a few edges different.
no code implementations • 10 Jun 2017 • Kun Zhang, Mingming Gong, Joseph Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour
This problem has received much attention in multiple fields, but it is not clear to what extent the causal model for the measurement-error-free variables can be identified in the presence of measurement error with unknown variance.
no code implementations • 3 Oct 2016 • Joseph Ramsey
A number of attempts have been made to improve accuracy and/or scalability of the PC (Peter and Clark) algorithm, some well known (Buhlmann, et al., 2010; Kalisch and Buhlmann, 2007; 2008; Zhang, 2012, to give some examples).