48 citations · 188 across the 17 of their papers we have counts for
6 papers · 1 filter
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…
Fast Conditional Independence Test for Vector Variables with Large Sample Sizes
Krzysztof Chalupka, Pietro Perona, Frederick Eberhardt
We present and evaluate the Fast (conditional) Independence Test (FIT) -- a nonparametric conditional independence test. The test is based on the idea that when $P(X \mid Y, Z) = P…
Estimating Causal Direction and Confounding of Two Discrete Variables
Krzysztof Chalupka, Frederick Eberhardt, Pietro Perona
We propose a method to classify the causal relationship between two discrete variables given only the joint distribution of the variables, acknowledging that the method is subject…
Unsupervised Discovery of El Nino Using Causal Feature Learning on Microlevel Climate Data
Krzysztof Chalupka, Tobias Bischoff, Pietro Perona +1
We show that the climate phenomena of El Nino and La Nina arise naturally as states of macro-variables when our recent causal feature learning framework (Chalupka 2015, Chalupka 20…
Multi-Level Cause-Effect Systems
Krzysztof Chalupka, Pietro Perona, Frederick Eberhardt
We present a domain-general account of causation that applies to settings in which macro-level causal relations between two systems are of interest, but the relevant causal feature…
Visual Causal Feature Learning
Krzysztof Chalupka, Pietro Perona, Frederick Eberhardt
We provide a rigorous definition of the visual cause of a behavior that is broadly applicable to the visually driven behavior in humans, animals, neurons, robots and other perceivi…