56 citations · 316 across the 22 of their papers we have counts for
22 papers
Py-Tetrad and RPy-Tetrad: A New Python Interface with R Support for Tetrad Causal Search
Joseph D. Ramsey, Bryan Andrews
We give novel Python and R interfaces for the (Java) Tetrad project for causal modeling, search, and estimation. The Tetrad project is a mainstay in the literature, having been und…
Causal-learn: Causal Discovery in Python
Yujia Zheng, Biwei Huang, Wei Chen +6
Causal discovery aims at revealing causal relations from observational data, which is a fundamental task in science and engineering. We describe , an open-so…
Greedy Relaxations of the Sparsest Permutation Algorithm
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 Ko…
Causal discovery for observational sciences using supervised machine learning
Anne Helby Petersen, Joseph Ramsey, Claus Thorn Ekstrøm +1
Causal inference can estimate causal effects, but unless data are collected experimentally, statistical analyses must rely on pre-specified causal models. Causal discovery algorith…
Improving Accuracy of Permutation DAG Search using Best Order Score Search
Joseph D. Ramsey
The Sparsest Permutation (SP) algorithm is accurate but limited to about 9 variables in practice; the Greedy Sparest Permutation (GSP) algorithm is faster but less weak theoretical…
FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders
Wei Chen, Kun Zhang, Ruichu Cai +4
We consider the problem of estimating a particular type of linear non-Gaussian model. Without resorting to the overcomplete Independent Component Analysis (ICA), we show that under…
Identification of Effective Connectivity Subregions
Ruben Sanchez-Romero, Joseph D. Ramsey, Kun Zhang +1
Standard fMRI connectivity analyses depend on aggregating the time series of individual voxels within regions of interest (ROIs). In certain cases, this spatial aggregation implies…
Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data
Biwei Huang, Kun Zhang, Ruben Sanchez-Romero +3
Autism spectrum disorder (ASD) is one of the major developmental disorders affecting children. Recently, it has been hypothesized that ASD is associated with atypical brain connect…
Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changes
Biwei Huang, Kun Zhang, Jiji Zhang +4
It is commonplace to encounter heterogeneous or nonstationary data, of which the underlying generating process changes across domains or over time. Such a distribution shift featur…
FASK with Interventional Knowledge Recovers Edges from the Sachs Model
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 differ…
A Comparison of Public Causal Search Packages on Linear, Gaussian Data with No Latent Variables
Joseph D. Ramsey, Bryan Andrews
We compare Tetrad (Java) algorithms to the other public software packages BNT (Bayes Net Toolbox, Matlab), pcalg (R), bnlearn (R) on the \vanilla" task of recovering DAG structure…
Causal Discovery in the Presence of Measurement Error: Identifiability Conditions
Kun Zhang, Mingming Gong, Joseph Ramsey +3
Measurement error in the observed values of the variables can greatly change the output of various causal discovery methods. This problem has received much attention in multiple fi…
Mixed Graphical Models for Causal Analysis of Multi-modal Variables
Andrew J Sedgewick, Joseph D. Ramsey, Peter Spirtes +2
Graphical causal models are an important tool for knowledge discovery because they can represent both the causal relations between variables and the multivariate probability distri…
Improving Accuracy and Scalability of the PC Algorithm by Maximizing P-value
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, 200…
algcomparison: Comparing the Performance of Graphical Structure Learning Algorithms with TETRAD
Joseph D. Ramsey, Daniel Malinsky, Kevin V. Bui
In this report we describe a tool for comparing the performance of graphical causal structure learning algorithms implemented in the TETRAD freeware suite of causal analysis method…
Scale-Free Exponents of Resting State provide a Biomarker for Typical and Atypical Brain Activity
S. J. Hanson, D. Mastrovito, C. Hanson +2
Scale-free networks (SFN) arise from simple growth processes, which can encourage efficient, centralized and fault tolerant communication (1). Recently its been shown that stable n…
Scaling up Greedy Causal Search for Continuous Variables
Joseph D. Ramsey
As standardly implemented in R or the Tetrad program, causal search algorithms used most widely or effectively by scientists have severe dimensionality constraints that make them i…
Effects of Nonparanormal Transform on PC and GES Search Accuracies
Joseph D. Ramsey
Liu, et al., 2009 developed a transformation of a class of non-Gaussian univariate distributions into Gaussian distributions. Liu and collaborators (2012) subsequently applied the…
A Scalable Conditional Independence Test for Nonlinear, Non-Gaussian Data
Joseph D. Ramsey
Many relations of scientific interest are nonlinear, and even in linear systems distributions are often non-Gaussian, for example in fMRI BOLD data. A class of search procedures fo…
Adjacency-Faithfulness and Conservative Causal Inference
Joseph Ramsey, Jiji Zhang, Peter L. Spirtes
Most causal inference algorithms in the literature (e.g., Pearl (2000), Spirtes et al. (2000), Heckerman et al. (1999)) exploit an assumption usually referred to as the causal Fait…
Discovering Cyclic Causal Models by Independent Components Analysis
Gustavo Lacerda, Peter L. Spirtes, Joseph Ramsey +1
We generalize Shimizu et al's (2006) ICA-based approach for discovering linear non-Gaussian acyclic (LiNGAM) Structural Equation Models (SEMs) from causally sufficient, continuous-…
Causal discovery of linear acyclic models with arbitrary distributions
Patrik O. Hoyer, Aapo Hyvarinen, Richard Scheines +4
An important task in data analysis is the discovery of causal relationships between observed variables. For continuous-valued data, linear acyclic causal models are commonly used t…