55 citations · 167 across the 40 of their papers we have counts for
18 papers · 1 filter
Causal Imitation Learning Under Measurement Error and Distribution Shift
Shi Bo, AmirEmad Ghassami
We study offline imitation learning (IL) when part of the decision-relevant state is observed only through noisy measurements and the distribution may change between training and d…
Causal Discovery in Linear Models with Unobserved Variables and Measurement Error
Yuqin Yang, Mohamed Nafea, Negar Kiyavash +2
The presence of unobserved common causes and measurement error poses two major obstacles to causal structure learning, since ignoring either source of complexity can induce spuriou…
Causal Discovery in Linear Latent Variable Models Subject to Measurement Error
Yuqin Yang, AmirEmad Ghassami, Mohamed Nafea +3
We focus on causal discovery in the presence of measurement error in linear systems where the mixing matrix, i.e., the matrix indicating the independent exogenous noise terms perta…
A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models
Ehsan Mokhtarian, Saber Salehkaleybar, AmirEmad Ghassami +1
We study experiment design for unique identification of the causal graph of a simple SCM, where the graph may contain cycles. The presence of cycles in the structure introduces maj…
Causal Discovery in Linear Structural Causal Models with Deterministic Relations
Yuqin Yang, Mohamed Nafea, AmirEmad Ghassami +1
Linear structural causal models (SCMs) -- in which each observed variable is generated by a subset of the other observed variables as well as a subset of the exogenous sources -- a…
Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias
Sina Akbari, Ehsan Mokhtarian, AmirEmad Ghassami +1
We consider the problem of learning the causal MAG of a system from observational data in the presence of latent variables and selection bias. Constraint-based methods are one of t…
Information Theoretic Measures for Fairness-aware Feature Selection
Sajad Khodadadian, Mohamed Nafea, AmirEmad Ghassami +1
Machine learning algorithms are increasingly used for consequential decision making regarding individuals based on their relevant features. Features that are relevant for accurate…
Impact of Data Processing on Fairness in Supervised Learning
Sajad Khodadadian, AmirEmad Ghassami, Negar Kiyavash
We study the impact of pre and post processing for reducing discrimination in data-driven decision makers. We first analyze the fundamental trade-off between fairness and accuracy…
A Recursive Markov Boundary-Based Approach to Causal Structure Learning
Ehsan Mokhtarian, Sina Akbari, AmirEmad Ghassami +1
Constraint-based methods are one of the main approaches for causal structure learning that are particularly valued as they are asymptotically guaranteed to find a structure that is…
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs
Ignavier Ng, AmirEmad Ghassami, Kun Zhang
Learning graphical structures based on Directed Acyclic Graphs (DAGs) is a challenging problem, partly owing to the large search space of possible graphs. A recent line of work for…
Model-Augmented Estimation of Conditional Mutual Information for Feature Selection
Alan Yang, AmirEmad Ghassami, Maxim Raginsky +2
Markov blanket feature selection, while theoretically optimal, is generally challenging to implement. This is due to the shortcomings of existing approaches to conditional independ…
Interventional Experiment Design for Causal Structure Learning
AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash
It is known that from purely observational data, a causal DAG is identifiable only up to its Markov equivalence class, and for many ground truth DAGs, the direction of a large port…
Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs
AmirEmad Ghassami, Alan Yang, Negar Kiyavash +1
The main approach to defining equivalence among acyclic directed causal graphical models is based on the conditional independence relationships in the distributions that the causal…
Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables
Saber Salehkaleybar, AmirEmad Ghassami, Negar Kiyavash +1
We consider the problem of learning causal models from observational data generated by linear non-Gaussian acyclic causal models with latent variables. Without considering the effe…
Fairness in Supervised Learning: An Information Theoretic Approach
AmirEmad Ghassami, Sajad Khodadadian, Negar Kiyavash
Automated decision making systems are increasingly being used in real-world applications. In these systems for the most part, the decision rules are derived by minimizing the train…
Budgeted Experiment Design for Causal Structure Learning
AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash +1
We study the problem of causal structure learning when the experimenter is limited to perform at most non-adaptive experiments of size . We formulate the problem of finding…
Learning Causal Structures Using Regression Invariance
AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash +1
We study causal inference in a multi-environment setting, in which the functional relations for producing the variables from their direct causes remain the same across environments…
Optimal Experiment Design for Causal Discovery from Fixed Number of Experiments
AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash
We study the problem of causal structure learning over a set of random variables when the experimenter is allowed to perform at most experiments in a non-adaptive manner. We co…