Add to ORKGCounting and sampling directed acyclic graphs from a Markov equivalence class are fundamental tasks in graphical causal analysis. In this paper we show that these tasks can be performed in polynomial time, solving a long-standing open problem in this area. Our algorithms are effective and easily implementable. As we show in experiments, these breakthroughs make thought-to-be-infeasible strategies in active learning of causal structures and causal effect identification with regard to a Markov equivalence class practically applicable.Comment: Preliminary results of this work have been presented at the AAAI Conference on Artificial Intelligence (AAAI 2021), see arXiv:2012.0967
Thesis: S.M., Massachusetts Institute of Technology, Department of Biological Engineering, February,...
In practice the vast majority of causal effect estimations from observational data are computed usin...
Inferring the causal structure that links n observables is usually based upon detecting statistical ...
Counting and uniform sampling of directed acyclic graphs (DAGs) from a Markov equivalence class are ...
The causal relationships among a set of random variables are commonly represented by a Directed Acyc...
Exploring directed acyclic graphs (DAGs) in a Markov equivalence class is pivotal to infer causal ef...
A polynomial-time exact algorithm for counting the number of directed acyclic graphs in a Markov equ...
We introduce a new method to estimate the Markov equivalence class of a directed acyclic graph (DAG)...
Enumerating the directed acyclic graphs (DAGs) of a Markov equivalence class (MEC) is an important p...
Causal structure learning from observational data remains a non-trivial task due to various factors ...
When learning a directed acyclic graph (DAG) model via observational data, one generally cannot iden...
Causal graphs, such as directed acyclic graphs (DAGs) and partial ancestral graphs (PAGs), represent...
Because causal learning from observational data cannot avoid the inherent indistinguishability for c...
When learning a directed acyclic graph (DAG) model via observational data, one generally cannot iden...
There have been many efforts to identify causal graphical features such as directed edges between ra...
Thesis: S.M., Massachusetts Institute of Technology, Department of Biological Engineering, February,...
In practice the vast majority of causal effect estimations from observational data are computed usin...
Inferring the causal structure that links n observables is usually based upon detecting statistical ...
Counting and uniform sampling of directed acyclic graphs (DAGs) from a Markov equivalence class are ...
The causal relationships among a set of random variables are commonly represented by a Directed Acyc...
Exploring directed acyclic graphs (DAGs) in a Markov equivalence class is pivotal to infer causal ef...
A polynomial-time exact algorithm for counting the number of directed acyclic graphs in a Markov equ...
We introduce a new method to estimate the Markov equivalence class of a directed acyclic graph (DAG)...
Enumerating the directed acyclic graphs (DAGs) of a Markov equivalence class (MEC) is an important p...
Causal structure learning from observational data remains a non-trivial task due to various factors ...
When learning a directed acyclic graph (DAG) model via observational data, one generally cannot iden...
Causal graphs, such as directed acyclic graphs (DAGs) and partial ancestral graphs (PAGs), represent...
Because causal learning from observational data cannot avoid the inherent indistinguishability for c...
When learning a directed acyclic graph (DAG) model via observational data, one generally cannot iden...
There have been many efforts to identify causal graphical features such as directed edges between ra...
Thesis: S.M., Massachusetts Institute of Technology, Department of Biological Engineering, February,...
In practice the vast majority of causal effect estimations from observational data are computed usin...
Inferring the causal structure that links n observables is usually based upon detecting statistical ...