Search Results for author:
Found 17 papers, 9 papers with code
Invariant Subspace Decomposition
We consider the task of predicting a response Y from a set of covariates X in settings where the conditional distribution of Y given X changes over time.
Extrapolation-Aware Nonparametric Statistical Inference
In this work, we extend the nonparametric statistical model to explicitly allow for extrapolation and introduce a class of extrapolation assumptions that can be combined with existing inference techniques to draw extrapolation-aware conclusions.
Perturbation-based Analysis of Compositional Data
Existing statistical methods for compositional data analysis are inadequate for many modern applications for two reasons.
Boosted Control Functions
In particular, we establish a novel connection between the field of distribution generalization from machine learning, and simultaneous equation models and control function from econometrics.
Identifying Representations for Intervention Extrapolation
In this paper, we consider the task of intervention extrapolation: predicting how interventions affect an outcome, even when those interventions are not observed at training time, and show that identifiable representations can provide an effective solution to this task even if the interventions affect the outcome non-linearly.
Effect-Invariant Mechanisms for Policy Generalization
A major challenge in policy learning is how to adapt efficiently to unseen environments or tasks.
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.
Model-based Reinforcement Learning
reinforcement-learning
+1
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 adopt the concept of invariance from the causality literature and introduce the notion of policy invariance.
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.
