5 papers
Evaluating Bivariate Causal Statements Based on Mutual Compatibility
Erik Jahn, Dominik Janzing
For many real-world systems, causal ground truth is difficult to obtain, making claims about causal effects hard to assess. We develop methods for evaluating collections of $\binom…
Lower Bounds on the Size of Markov Equivalence Classes
Erik Jahn, Frederick Eberhardt, Leonard J. Schulman
Causal discovery algorithms typically recover causal graphs only up to their Markov equivalence classes unless additional parametric assumptions are made. The sizes of these equiva…
Causal Identification in Time Series Models
Erik Jahn, Karthik Karnik, Leonard J. Schulman
In this paper, we analyze the applicability of the Causal Identification algorithm to causal time series graphs with latent confounders. Since these graphs extend over infinitely m…
Identification of Mixtures of Discrete Product Distributions in Near-Optimal Sample and Time Complexity
Spencer L. Gordon, Erik Jahn, Bijan Mazaheri +2
We consider the problem of identifying, from statistics, a distribution of discrete random variables that is a mixture of product distributions. The best previ…
2-factors with k cycles in Hamiltonian graphs
Matija Bucić, Erik Jahn, Alexey Pokrovskiy +1
A well known generalisation of Dirac's theorem states that if a graph on vertices has minimum degree at least then contains a -factor consisting of exact…