Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications

Code

• github.com/mwien/counting-with-applications
  OfficialIn papernone★ 1
• github.com/cxjdavin/active-causal-structure-learning-with-advice
  none★ 0

Abstract

Counting 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.

Tasks

Reproductions