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04/10/2023
Partial Identification of Causal Effects Using Proxy Variables
Proximal causal inference is a recently proposed framework for evaluatin...
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05/20/2022
A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models
We study experiment design for the unique identification of the causal g...
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01/26/2022
Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects
We consider the task of identifying and estimating the causal effect of ...
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11/04/2021
Proximal Causal Inference with Hidden Mediators: Front-Door and Related Mediation Problems
Proximal causal inference was recently proposed as a framework to identi...
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10/30/2021
Causal Discovery in Linear Structural Causal Models with Deterministic Relations
Linear structural causal models (SCMs) – in which each observed variable...
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10/24/2021
Partially Intervenable Causal Models
Graphical causal models led to the development of complete non-parametri...
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10/22/2021
Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias
We consider the problem of learning the causal MAG of a system from obse...
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06/01/2021
Information Theoretic Measures for Fairness-aware Feature Selection
Machine learning algorithms are increasingly used for consequential deci...
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05/19/2021
Multiply Robust Causal Mediation Analysis with Continuous Treatments
In many applications, researchers are interested in the direct and indir...
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04/07/2021
Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals
A moment function is called doubly robust if it is comprised of two nuis...
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02/03/2021
Impact of Data Processing on Fairness in Supervised Learning
We study the impact of pre and post processing for reducing discriminati...
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10/10/2020
A Recursive Markov Blanket-Based Approach to Causal Structure Learning
One of the main approaches for causal structure learning is constraint-b...
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06/17/2020
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs
Learning graphical structure based on Directed Acyclic Graphs (DAGs) is ...
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11/12/2019
Model-Augmented Nearest-Neighbor Estimation of Conditional Mutual Information for Feature Selection
Markov blanket feature selection, while theoretically optimal, generally...
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10/28/2019
Characterizing Distribution Equivalence for Cyclic and Acyclic Directed Graphs
The main way for defining equivalence among acyclic directed graphs is b...
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10/12/2019
Interventional Experiment Design for Causal Structure Learning
It is known that from purely observational data, a causal DAG is identif...
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08/11/2019
Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables
We consider the problem of learning causal models from observational dat...
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06/04/2018
REORDER: Securing Dynamic-Priority Real-Time Systems Using Schedule Obfuscation
Modern real-time systems (RTS) are increasingly the focus of security th...
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02/05/2018
Counting and Uniform Sampling from Markov Equivalent DAGs
We propose an exact solution for the problem of finding the size of a Ma...
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01/13/2018
Fairness in Supervised Learning: An Information Theoretic Approach
Automated decision making systems are increasingly being used in real-wo...
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09/11/2017
Budgeted Experiment Design for Causal Structure Learning
We study the problem of causal structure learning when the experimenter ...
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05/26/2017
Learning Causal Structures Using Regression Invariance
We study causal inference in a multi-environment setting, in which the f...
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05/07/2017
A Reconnaissance Attack Mechanism for Fixed-Priority Real-Time Systems
In real-time embedded systems (RTS), failures due to security breaches c...
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02/27/2017
Optimal Experiment Design for Causal Discovery from Fixed Number of Experiments
We study the problem of causal structure learning over a set of random v...
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01/30/2017
