pcalg
R package pcalg: Estimation of CPDAG/PAG and causal inference using the IDA algorithm , Standard and robust estimation of the equivalence class of a Directed Acyclic Graph (DAG) via the PC-Algorithm. The equivalence class is represented by its (unique) Completete Partially Directed Acyclic Graph (CPDAG). Furthermore, a PAG instead of a CPDAG can be estimated if latent variables and/or selection variables are assumed to be present. FCI and RFCI are available for estimating PAGs. Functions for causal inference using the IDA algorithm (based on do-calculus of Judea Pearl) are provided for CPDAGs.
(Source: http://cran.r-project.org/web/packages)
Keywords for this software
References in zbMATH (referenced in 65 articles , 3 standard articles )
Showing results 1 to 20 of 65.
Sorted by year (- Champion, Magali; Picheny, Victor; Vignes, Matthieu: Inferring large graphs using $\ell_1$-penalized likelihood (2018)
- Md. Bahadur Badsha, Evan A Martin, Audrey Qiuyan Fu: MRPC: An R package for accurate inference of causal graphs (2018) arXiv
- Nandy, Preetam; Hauser, Alain; Maathuis, Marloes H.: High-dimensional consistency in score-based and hybrid structure learning (2018)
- Rothenhäusler, Dominik; Ernest, Jan; Bühlmann, Peter: Causal inference in partially linear structural equation models (2018)
- Runge, J.: Causal network reconstruction from time series: from theoretical assumptions to practical estimation (2018)
- Isozaki, Takashi; Kuroki, Manabu: Learning causal graphs with latent confounders in weak faithfulness violations (2017)
- Malinsky, Daniel; Spirtes, Peter: Estimating bounds on causal effects in high-dimensional and possibly confounded systems (2017)
- Park, Young Woong; Klabjan, Diego: Bayesian network learning via topological order (2017)
- Pircalabelu, Eugen; Claeskens, Gerda; Gijbels, Irène: Copula directed acyclic graphs (2017)
- Santtu Tikka and Juha Karvanen: Identifying Causal Effects with the R Package causaleffect (2017)
- Silva, Ricardo; Shimizu, Shohei: Learning instrumental variables with structural and non-Gaussianity assumptions (2017)
- Aghdam, Rosa; Alijanpour, Mohsen; Azadi, Mehrdad; Ebrahimi, Ali; Eslahchi, Changiz; Rezvan, Abolfazl: Inferring gene regulatory networks by PCA-CMI using Hill climbing algorithm based on MIT score and SORDER method (2016)
- Goudie, Robert J. B.; Mukherjee, Sach: A Gibbs sampler for learning DAGs (2016)
- Ha, Min Jin; Sun, Wei; Xie, Jichun: $\mathsfPenPC$: a two-step approach to estimate the skeletons of high-dimensional directed acyclic graphs (2016)
- Hobæk Haff, Ingrid; Aas, Kjersti; Frigessi, Arnoldo; Lacal, Virginia: Structure learning in Bayesian networks using regular vines (2016)
- Lan, Wei; Ding, Yue; Fang, Zheng; Fang, Kuangnan: Testing covariates in high dimension linear regression with latent factors (2016)
- Lan, Wei; Zhong, Ping-Shou; Li, Runze; Wang, Hansheng; Tsai, Chih-Ling: Testing a single regression coefficient in high dimensional linear models (2016)
- Oates, Chris. J.; Smith, Jim Q.; Mukherjee, Sach: Estimating causal structure using conditional DAG models (2016)
- Peña, Jose M.; Gómez-Olmedo, Manuel: Learning marginal AMP chain graphs under faithfulness revisited (2016)
- Zhang, Jiji; Spirtes, Peter: The three faces of faithfulness (2016)