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.
Questions in causality, control, and reinforcement learning go beyond the classical machine learning task of prediction under i. i. d.
Most of the existing estimators assume that the error term in the response Y and the hidden confounders are uncorrelated with the instruments Z.
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.
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
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
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.
Results on both simulated and real-world examples suggest that learning the structure of kinetic systems benefits from a causal perspective.
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.
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.