Estimating high-dimensional intervention effects from observational data
From MaRDI portal
Publication:1043733
DOI10.1214/09-AOS685zbMath1191.62118arXiv0810.4214OpenAlexW2050502901WikidataQ57707432 ScholiaQ57707432MaRDI QIDQ1043733
Markus Kalisch, Marloes H. Maathuis, Peter Bühlmann
Publication date: 9 December 2009
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/0810.4214
sparsitygraphical modelingPC-algorithmintervention calculuscausal analysisdirected acyclic graph (DAG)riboflavin data
Multivariate analysis (62H99) Applications of graph theory (05C90) Directed graphs (digraphs), tournaments (05C20)
Related Items
A local method for identifying causal relations under Markov equivalence, Switching Regression Models and Causal Inference in the Presence of Discrete Latent Variables, Sufficient dimension reduction for average causal effect estimation, Unnamed Item, Causal statistical inference in high dimensions, Causal inference in genetic trio studies, Geometry of the faithfulness assumption in causal inference, \(\ell_{0}\)-penalized maximum likelihood for sparse directed acyclic graphs, An efficient algorithm for counting Markov equivalent DAGs, Estimating bounds on causal effects in high-dimensional and possibly confounded systems, Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs, Bayesian inference of causal effects from observational data in Gaussian graphical models, Fast causal orientation learning in directed acyclic graphs, The dual PC algorithm and the role of Gaussianity for structure learning of Bayesian networks, Improved baselines for causal structure learning on interventional data, Capturing ridge functions in high dimensions from point queries, Bayesian causal inference in probit graphical models, Marginal integration for nonparametric causal inference, High dimensional sparse covariance estimation via directed acyclic graphs, Learning high-dimensional directed acyclic graphs with latent and selection variables, Invariance, causality and robustness, Inferring gene regulatory networks by an order independent algorithm using incomplete data sets, Model free estimation of graphical model using gene expression data, High-dimensional consistency in score-based and hybrid structure learning, Efficient Sampling and Structure Learning of Bayesian Networks, Complete Graphical Characterization and Construction of Adjustment Sets in Markov Equivalence Classes of Ancestral Graphs, Structural learning and estimation of joint causal effects among network-dependent variables, Testing conditional independence in supervised learning algorithms, Network modeling in biology: statistical methods for gene and brain networks, Objective Bayes model selection of Gaussian interventional essential graphs for the identification of signaling pathways, PenPC : A two-step approach to estimate the skeletons of high-dimensional directed acyclic graphs, Foundations of structural causal models with cycles and latent variables, Approximation of generalized ridge functions in high dimensions, Robust Causal Structure Learning with Some Hidden Variables, Unnamed Item, Beyond the mean: a flexible framework for studying causal effects using linear models, Log‐mean Linear Parameterization for Discrete Graphical Models of Marginal Independence and the Analysis of Dichotomizations, Unnamed Item, Discussion to: Bayesian graphical models for modern biological applications by Y. Ni, V. Baladandayuthapani, M. Vannucci and F.C. Stingo, Inferring network structure from interventional time-course experiments, A generalized back-door criterion, Discussion of big Bayes stories and BayesBag
Uses Software
Cites Work
- On rigid circuit graphs
- A note on the Lasso for Gaussian graphical model selection
- Causation, prediction, and search
- Confounding and collapsibility in causal inference
- Bayesian analysis in expert systems. With comments and a rejoinder by the authors
- Ancestral graph Markov models.
- Statistics and causal inference: A review. (With discussion)
- Learning Bayesian networks: The combination of knowledge and statistical data
- Weak convergence and empirical processes. With applications to statistics
- Incidence matrices and interval graphs
- High-dimensional graphs and variable selection with the Lasso
- Uniform consistency in causal inference
- On the Desirability of Acyclic Database Schemes
- Statistics and Causal Inference
- Causal diagrams for empirical research
- Causal Inference Without Counterfactuals
- 10.1162/153244302760200696
- 10.1162/153244303321897717
- Large-Scale Simultaneous Hypothesis Testing
- Approximating discrete probability distributions with dependence trees
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item