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Found 11 papers, 7 papers with code
Supervised Learning and Model Analysis with Compositional Data
We propose KernelBiome, a kernel-based nonparametric regression and classification framework for compositional data.
Identifiability of Sparse Causal Effects using Instrumental Variables
Exogenous heterogeneity, for example, in the form of instrumental variables can help us learn a system's underlying causal structure and predict the outcome of unseen intervention experiments.
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.
Exploiting Independent Instruments: Identification and Distribution Generalization
Most of the existing estimators assume that the error term in the response Y and the hidden confounders are uncorrelated with the instruments Z.
Invariant Policy Learning: A Causal Perspective
We view the environmental shift problem through the lens of causality and propose multi-environment contextual bandits that allow for changes in the underlying mechanisms.
A causal framework for distribution generalization
We introduce the formal framework of distribution generalization that allows us to analyze the above problem in partially observed nonlinear models for both direct interventions on $X$ and interventions that occur indirectly via exogenous variables $A$.
Methodology Primary 62Gxx, secondary 62G35, 62G08, 62D20
Causal models for dynamical systems
In this chapter, we provide a natural and straight-forward extension of this concept to dynamical systems, focusing on continuous time models.
Methodology Dynamical Systems
Stabilizing Variable Selection and Regression
In particular, it is useful to distinguish between stable and unstable predictors, i. e., predictors which have a fixed or a changing functional dependence on the response, respectively.
Methodology Applications
Learning stable and predictive structures in kinetic systems: Benefits of a causal approach
Results on both simulated and real-world examples suggest that learning the structure of kinetic systems benefits from a causal perspective.
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.
Kernel-based Tests for Joint Independence
Based on an empirical estimate of dHSIC, we define three different non-parametric hypothesis tests: a permutation test, a bootstrap test and a test based on a Gamma approximation.
