Search Results for author: Frederick Eberhardt

Found 9 papers, 1 papers with code

Approximate Causal Abstraction

no code implementations27 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.

ASP-based Discovery of Semi-Markovian Causal Models under Weaker Assumptions

no code implementations6 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.

Causal Discovery

Fast Conditional Independence Test for Vector Variables with Large Sample Sizes

1 code implementation8 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.

Estimating Causal Direction and Confounding of Two Discrete Variables

no code implementations4 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.

Unsupervised Discovery of El Nino Using Causal Feature Learning on Microlevel Climate Data

no code implementations30 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.

Causal Discovery from Subsampled Time Series Data by Constraint Optimization

no code implementations25 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.

Causal Discovery Time Series

Multi-Level Cause-Effect Systems

no code implementations25 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.

Visual Causal Feature Learning

no code implementations7 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.

Active Learning

Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure

no code implementations26 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.

Causal Discovery

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