3 papers
cs.AI2026
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…
stat.ML2025
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…
cs.LG2025
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…