Frederick Eberhardt
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View article: Lower Bounds on the Size of Markov Equivalence Classes
Lower Bounds on the Size of Markov Equivalence Classes Open
Causal discovery algorithms typically recover causal graphs only up to their Markov equivalence classes unless additional parametric assumptions are made. The sizes of these equivalence classes reflect the limits of what can be learned abo…
View article: Modeling Discrimination with Causal Abstraction
Modeling Discrimination with Causal Abstraction Open
A person is directly racially discriminated against only if her race caused her worse treatment. This implies that race is an attribute sufficiently separable from other attributes to isolate its causal role. But race is embedded in a nexu…
View article: Controlling for discrete unmeasured confounding in nonlinear causal models
Controlling for discrete unmeasured confounding in nonlinear causal models Open
Unmeasured confounding is a major challenge for identifying causal relationships from non-experimental data. Here, we propose a method that can accommodate unmeasured discrete confounding. Extending recent identifiability results in deep l…
View article: Unsupervised mapping of causal relations between brain lesions and behavior
Unsupervised mapping of causal relations between brain lesions and behavior Open
Human lesion studies offer one of the most direct routes to investigating the causal relations between brain regions and behavioral outcomes in circumstances where experimental interventions are highly restricted. However, these studies fa…
View article: Causal Emergence: When Distortions in a Map Obscure the Territory
Causal Emergence: When Distortions in a Map Obscure the Territory Open
We provide a critical assessment of the account of causal emergence presented in Erik Hoel’s 2017 article “When the map is better than the territory”. The account integrates causal and information theoretic concepts to explain under what c…
View article: Intracranial electrical stimulation alters meso-scale network integration as a function of network topology
Intracranial electrical stimulation alters meso-scale network integration as a function of network topology Open
Human brain dynamics are organized into a multi-scale network structure that contains multiple tight-knit, meso-scale communities. Recent work has demonstrated that many psychological capacities, as well as impairments in cognitive functio…
View article: ASP-based Discovery of Semi-Markovian Causal Models under Weaker Assumptions
ASP-based Discovery of Semi-Markovian Causal Models under Weaker Assumptions Open
In recent years the possibility of relaxing the so-called Faithfulness assumption in automated causal discovery has been investigated. The investigation showed (1) that the Faithfulness assumption can be weakened in various ways that in an…
View article: Approximate Causal Abstraction
Approximate Causal Abstraction Open
Scientific models describe natural phenomena at different levels of abstraction. Abstract descriptions can provide the basis for interventions on the system and explanation of observed phenomena at a level of granularity that is coarser th…
View article: ASP-based Discovery of Semi-Markovian Causal Models under Weaker Assumptions
ASP-based Discovery of Semi-Markovian Causal Models under Weaker Assumptions Open
In recent years the possibility of relaxing the so-called Faithfulness assumption in automated causal discovery has been investigated. The investigation showed (1) that the Faithfulness assumption can be weakened in various ways that in an…
View article: Fast Conditional Independence Test for Vector Variables with Large Sample Sizes
Fast Conditional Independence Test for Vector Variables with Large Sample Sizes Open
We present and evaluate the Fast (conditional) Independence Test (FIT) -- a nonparametric conditional independence test. The test is based on the idea that when $P(X \mid Y, Z) = P(X \mid Y)$, $Z$ is not useful as a feature to predict $X$,…
View article: Causal Mapping of Emotion Networks in the Human Brain: Framework and Preliminary Findings
Causal Mapping of Emotion Networks in the Human Brain: Framework and Preliminary Findings Open
Emotions involve many cortical and subcortical regions, prominently including the amygdala. It remains unknown how these multiple network components interact, and it remains unknown how they cause the behavioral, autonomic, and experientia…
View article: SAT-based causal discovery under weaker assumptions
SAT-based causal discovery under weaker assumptions Open
Using the flexibility of recently developed methods for causal discovery based on Boolean satisfiability (SAT) solvers, we encode a variety of assumptions that weaken the Faithfulness assumption. The encoding results in a number of SAT-bas…
View article: Estimating Causal Direction and Confounding of Two Discrete Variables
Estimating Causal Direction and Confounding of Two Discrete Variables Open
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. We assume that the causal sy…
View article: Unsupervised Discovery of El Nino Using Causal Feature Learning on Microlevel Climate Data
Unsupervised Discovery of El Nino Using Causal Feature Learning on Microlevel Climate Data Open
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) a…
View article: Unsupervised Discovery of El Nino Using Causal Feature Learning on\n Microlevel Climate Data
Unsupervised Discovery of El Nino Using Causal Feature Learning on\n Microlevel Climate Data Open
We show that the climate phenomena of El Nino and La Nina arise naturally as\nstates of macro-variables when our recent causal feature learning framework\n(Chalupka 2015, Chalupka 2016) is applied to micro-level measures of zonal wind\n(ZW…
View article: Causal Discovery from Subsampled Time Series Data by Constraint Optimization
Causal Discovery from Subsampled Time Series Data by Constraint Optimization Open
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. Previous work has shown that such subsampling can lead to…
View article: Multi-Level Cause-Effect Systems
Multi-Level Cause-Effect Systems Open
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 features are poorly understood and have to be aggregated from vast…