Reproduce “Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
AmirEmad Ghassami, Alan Yang, Negar Kiyavash, Kun Zhang
The authors define observational equivalence for linear Gaussian causal graphs, including feedback, as equality of the sets of distributions they can generate. Their graphical test reduces this comparison to finite graph operations. Reversing a cycle and redirecting specified incoming outside edges preserves that set. Under stated assumptions, the global score minimizer is asymptotically quasi-equivalent to the ground truth.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.