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Found 17 papers, 10 papers with code
Adjustment Identification Distance: A gadjid for Causal Structure Learning
Evaluating graphs learned by causal discovery algorithms is difficult: The number of edges that differ between two graphs does not reflect how the graphs differ with respect to the identifying formulas they suggest for causal effects.
Unfair Utilities and First Steps Towards Improving Them
Many fairness criteria constrain the policy or choice of predictors.
Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past
In this paper, we outline the difficulties that arise due to time structure and propose methodology for constructing identifying equations that can be used for consistent parametric estimation of causal effects in time series data.
Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning
Questions in causality, control, and reinforcement learning go beyond the classical machine learning task of prediction under i. i. d.
Model-based Reinforcement Learning
reinforcement-learning
+1
Compositional Abstraction Error and a Category of Causal Models
Interventional causal models describe several joint distributions over some variables used to describe a system, one for each intervention setting.
Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Game
Here, we show that marginal variance tends to increase along the causal order for generically sampled additive noise models.
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values
In this article, we describe the algorithms for causal structure learning from time series data that won the Causality 4 Climate competition at the Conference on Neural Information Processing Systems 2019 (NeurIPS).
Causality in cognitive neuroscience: concepts, challenges, and distributional robustness
Robustness (or invariance) is a fundamental principle underlying causal methodology.
Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise
We introduce coroICA, confounding-robust independent component analysis, a novel ICA algorithm which decomposes linearly mixed multivariate observations into independent components that are corrupted (and rendered dependent) by hidden group-wise stationary confounding.
Causal Consistency of Structural Equation Models
Complex systems can be modelled at various levels of detail.
A note on the expected minimum error probability in equientropic channels
While the channel capacity reflects a theoretical upper bound on the achievable information transmission rate in the limit of infinitely many bits, it does not characterise the information transfer of a given encoding routine with finitely many bits.
Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data
Causal inference concerns the identification of cause-effect relationships between variables.
Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation
Optimization on manifolds is a class of methods for optimization of an objective function, subject to constraints which are smooth, in the sense that the set of points which satisfy the constraints admits the structure of a differentiable manifold.
Causal and anti-causal learning in pattern recognition for neuroimaging
Pattern recognition in neuroimaging distinguishes between two types of models: encoding- and decoding models.
Decoding index finger position from EEG using random forests
While invasively recorded brain activity is known to provide detailed information on motor commands, it is an open question at what level of detail information about positions of body parts can be decoded from non-invasively acquired signals.
MERLiN: Mixture Effect Recovery in Linear Networks
Causal inference concerns the identification of cause-effect relationships between variables, e. g. establishing whether a stimulus affects activity in a certain brain region.
Causal interpretation rules for encoding and decoding models in neuroimaging
Causal terminology is often introduced in the interpretation of encoding and decoding models trained on neuroimaging data.
