Search Results for author:
Found 44 papers, 22 papers with code
The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology
The devices, which we call causal chambers, are computer-controlled laboratories that allow us to manipulate and measure an array of variables from these physical systems, providing a rich testbed for algorithms from a variety of fields.
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.
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.
Model-based causal feature selection for general response types
Discovering causal relationships from observational data is a fundamental yet challenging task.
Effect-Invariant Mechanisms for Policy Generalization
A major challenge in policy learning is how to adapt efficiently to unseen environments or tasks.
Unfair Utilities and First Steps Towards Improving Them
Many fairness criteria constrain the policy or choice of predictors.
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.
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
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 Ancestry Search
Recently, methods have been proposed that exploit the invariance of prediction models with respect to changing environments to infer subsets of the causal parents of a response variable.
Structure Learning for Directed Trees
Furthermore, we study the identifiability gap, which quantifies how much better the true causal model fits the observational distribution, and prove that it is lower bounded by local properties of the causal model.
Invariant Policy Learning: A Causal Perspective
We adopt the concept of invariance from the causality literature and introduce the notion of policy invariance.
Regularizing towards Causal Invariance: Linear Models with Proxies
In the case of two proxy variables, we propose a modified estimator that is prediction optimal under interventions up to a known strength.
Conditional Independence Testing in Hilbert Spaces with Applications to Functional Data Analysis
We study the problem of testing the null hypothesis that X and Y are conditionally independent given Z, where each of X, Y and Z may be functional random variables.
Statistics Theory Statistics Theory
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
Distributional robustness of K-class estimators and the PULSE
While causal models are robust in that they are prediction optimal under arbitrarily strong interventions, they may not be optimal when the interventions are bounded.
Causality in cognitive neuroscience: concepts, challenges, and distributional robustness
Robustness (or invariance) is a fundamental principle underlying causal methodology.
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
Causal discovery in heavy-tailed models
Causal questions are omnipresent in many scientific problems.
Methodology
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.
Anchor regression: heterogeneous data meets causality
If anchor regression and least squares provide the same answer (anchor stability), we establish that OLS parameters are invariant under certain distributional changes.
Methodology
Invariant Causal Prediction for Nonlinear Models
In this work, we present and evaluate an array of methods for nonlinear and nonparametric versions of ICP for learning the causal parents of given target variables.
Methodology
Foundations of Structural Causal Models with Cycles and Latent Variables
In this paper, we investigate SCMs in a more general setting, allowing for the presence of both latent confounders and cycles.
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.
Invariant Models for Causal Transfer Learning
We focus on the problem of Domain Generalization, in which no examples from the test task are observed.
backShift: Learning causal cyclic graphs from unknown shift interventions
We propose a simple method to learn linear causal cyclic models in the presence of latent variables.
Removing systematic errors for exoplanet search via latent causes
We describe a method for removing the effect of confounders in order to reconstruct a latent quantity of interest.
Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations
We describe a method to perform functional operations on probability distributions of random variables.
Causal inference using invariant prediction: identification and confidence intervals
In contrast, predictions from a non-causal model can potentially be very wrong if we actively intervene on variables.
Methodology
Distinguishing cause from effect using observational data: methods and benchmarks
We evaluate the performance of several bivariate causal discovery methods on these real-world benchmark data and in addition on artificially simulated data.
On the Intersection Property of Conditional Independence and its Application to Causal Discovery
This work investigates the intersection property of conditional independence.
Causal Inference on Time Series using Restricted Structural Equation Models
We study a class of restricted Structural Equation Models for time series that we call Time Series Models with Independent Noise (TiMINo).
CAM: Causal additive models, high-dimensional order search and penalized regression
We develop estimation for potentially high-dimensional additive structural equation models.
Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders
We propose a kernel method to identify finite mixtures of nonparametric product distributions.
Causal Discovery with Continuous Additive Noise Models
We consider the problem of learning causal directed acyclic graphs from an observational joint distribution.
Structural Intervention Distance (SID) for Evaluating Causal Graphs
To quantify such differences, we propose a (pre-) distance between DAGs, the structural intervention distance (SID).
Counterfactual Reasoning and Learning Systems
This work shows how to leverage causal inference to understand the behavior of complex learning systems interacting with their environment and predict the consequences of changes to the system.
On Causal and Anticausal Learning
We consider the problem of function estimation in the case where an underlying causal model can be inferred.
Identifiability of Gaussian structural equation models with equal error variances
In this work, we prove full identifiability if all noise variables have the same variances: the directed acyclic graph can be recovered from the joint Gaussian distribution.
Kernel-based Conditional Independence Test and Application in Causal Discovery
Conditional independence testing is an important problem, especially in Bayesian network learning and causal discovery.
Nonlinear causal discovery with additive noise models
The discovery of causal relationships between a set of observed variables is a fundamental problem in science.
