no code implementations • 26 Sep 2013 • Antti Hyttinen, Patrik O. Hoyer, Frederick Eberhardt, Matti Jarvisalo
We present a very general approach to learning the structure of causal models based on d-separation constraints, obtained from any given set of overlapping passive observational or experimental data sets.
no code implementations • 7 Dec 2014 • 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 perceiving systems.
no code implementations • 25 Dec 2015 • Krzysztof Chalupka, Pietro Perona, Frederick Eberhardt
We formalize the connection between micro- and macro-variables in such situations and provide a coherent framework describing causal relations at multiple levels of analysis.
no code implementations • 25 Feb 2016 • Antti Hyttinen, Sergey Plis, Matti Järvisalo, Frederick Eberhardt, David Danks
This paper focuses on causal structure estimation from time series data in which measurements are obtained at a coarser timescale than the causal timescale of the underlying system.
no code implementations • 30 May 2016 • Krzysztof Chalupka, Tobias Bischoff, Pietro Perona, Frederick Eberhardt
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 2016) is applied to micro-level measures of zonal wind (ZW) and sea surface temperatures (SST) taken over the equatorial band of the Pacific Ocean.
no code implementations • 4 Nov 2016 • 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 to an inherent baseline error.
1 code implementation • 8 Apr 2018 • Krzysztof Chalupka, Pietro Perona, Frederick Eberhardt
The test is based on the idea that when $P(X \mid Y, Z) = P(X \mid Y)$, $Z$ is not useful as a feature to predict $X$, as long as $Y$ is also a regressor.
no code implementations • 6 Jun 2019 • Zhalama, Jiji Zhang, Frederick Eberhardt, Wolfgang Mayer, Mark Junjie Li
In recent years the possibility of relaxing the so-called Faithfulness assumption in automated causal discovery has been investigated.
no code implementations • 27 Jun 2019 • Sander Beckers, Frederick Eberhardt, Joseph Y. Halpern
Abstract descriptions can provide the basis for interventions on the system and explanation of observed phenomena at a level of granularity that is coarser than the most fundamental account of the system.