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06/19/2023
Effect-Invariant Mechanisms for Policy Generalization
Policy learning is an important component of many real-world learning sy...
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06/01/2023
Unfair Utilities and First Steps Towards Improving Them
Many fairness criteria constrain the policy or choice of predictors. In ...
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03/17/2022
Identifiability of Sparse Causal Effects using Instrumental Variables
Exogenous heterogeneity, for example, in the form of instrumental variab...
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03/11/2022
Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past
Instrumental variable (IV) regression relies on instruments to infer cau...
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02/12/2022
Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning
Questions in causality, control, and reinforcement learning go beyond th...
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02/03/2022
Exploiting Independent Instruments: Identification and Distribution Generalization
Instrumental variable models allow us to identify a causal function betw...
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02/02/2022
Invariant Ancestry Search
Recently, methods have been proposed that exploit the invariance of pred...
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08/19/2021
Structure Learning for Directed Trees
Knowing the causal structure of a system is of fundamental interest in m...
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06/01/2021
Invariant Policy Learning: A Causal Perspective
In the past decade, contextual bandit and reinforcement learning algorit...
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05/22/2021
Statistical Testing under Distributional Shifts
Statistical hypothesis testing is a central problem in empirical inferen...
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03/03/2021
Regularizing towards Causal Invariance: Linear Models with Proxies
We propose a method for learning linear models whose predictive performa...
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01/18/2021
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 con...
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06/12/2020
The Difficult Task of Distribution Generalization in Nonlinear Models
We consider the problem of predicting a response from a set of covariate...
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05/18/2020
Towards Causal Inference for Spatio-Temporal Data: Conflict and Forest Loss in Colombia
In many data scientific problems, we are interested not only in modeling...
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05/07/2020
Distributional Robustness of K-class Estimators and the PULSE
In causal settings, such as instrumental variable settings, it is well k...
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02/14/2020
Distributional robustness as a guiding principle for causality in cognitive neuroscience
While probabilistic models describe the dependence structure between obs...
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01/17/2020
Causal models for dynamical systems
A probabilistic model describes a system in its observational state. In ...
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11/05/2019
Stabilizing Variable Selection and Regression
We consider regression in which one predicts a response Y with a set of ...
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08/14/2019
Causal discovery in heavy-tailed models
Causal questions are omnipresent in many scientific problems. While much...
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10/28/2018
Identifying Causal Structure in Large-Scale Kinetic Systems
In the natural sciences, differential equations are widely used to descr...
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08/16/2018
Switching Regression Models and Causal Inference in the Presence of Latent Variables
Given a response Y and a vector X = (X^1, ..., X^d) of d predictors, we ...
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04/19/2018
The Hardness of Conditional Independence Testing and the Generalised Covariance Measure
It is a common saying that testing for conditional independence, i.e., t...
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01/18/2018
Anchor regression: heterogeneous data meets causality
This is a preliminary draft of "Anchor regression: heterogeneous data me...
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11/18/2016
Structural Causal Models: Cycles, Marginalizations, Exogenous Reparametrizations and Reductions
Structural causal models (SCMs), also known as non-parametric structural...
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03/01/2016
Kernel-based Tests for Joint Independence
We investigate the problem of testing whether d random variables, which ...
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06/08/2015
backShift: Learning causal cyclic graphs from unknown shift interventions
We propose a simple method to learn linear causal cyclic models in the p...
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05/12/2015
Removing systematic errors for exoplanet search via latent causes
We describe a method for removing the effect of confounders in order to ...
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01/27/2015
Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations
We describe a method to perform functional operations on probability dis...
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12/11/2014
Distinguishing cause from effect using observational data: methods and benchmarks
The discovery of causal relationships from purely observational data is ...
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03/03/2014
On the Intersection Property of Conditional Independence and its Application to Causal Discovery
This work investigates the intersection property of conditional independ...
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10/06/2013
CAM: Causal additive models, high-dimensional order search and penalized regression
We develop estimation for potentially high-dimensional additive structur...
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09/26/2013
Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders
We propose a kernel method to identify finite mixtures of nonparametric ...
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09/26/2013
Causal Discovery with Continuous Additive Noise Models
We consider the problem of learning causal directed acyclic graphs from ...
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06/05/2013
Structural Intervention Distance (SID) for Evaluating Causal Graphs
Causal inference relies on the structure of a graph, often a directed ac...
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09/11/2012
Counterfactual Reasoning and Learning Systems
This work shows how to leverage causal inference to understand the behav...
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07/21/2012
Causal Inference on Time Series using Structural Equation Models
Causal inference uses observations to infer the causal structure of the ...
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06/27/2012
On Causal and Anticausal Learning
We consider the problem of function estimation in the case where an unde...
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05/11/2012
Identifiability of Gaussian structural equation models with equal error variances
We consider structural equation models in which variables can be written...
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05/09/2012
Identifying confounders using additive noise models
We propose a method for inferring the existence of a latent common cause...
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02/14/2012
Kernel-based Conditional Independence Test and Application in Causal Discovery
Conditional independence testing is an important problem, especially in ...
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02/14/2012
Identifiability of Causal Graphs using Functional Models
This work addresses the following question: Under what assumptions on th...
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02/14/2012
Detecting low-complexity unobserved causes
We describe a method that infers whether statistical dependences between...
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12/12/2011
Robust Learning via Cause-Effect Models
We consider the problem of function estimation in the case where the dat...
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11/02/2009