Publications by Negar Kiyavash

s-ID: Causal Effect Identification in a Sub-Population

Causal inference in a sub-population involves identifying the causal effect of an intervention on a specific subgroup, which is distinguished from the whole population through the influence of systematic biases in the sampling process. However, ignoring th ...

Association for the Advancement of Artificial Intelligence2024

Special Issue on Causality: Fundamental Limits and Applications

Negar Kiyavash

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Causal imitability under context-specific independence relations

Negar Kiyavash, Sina Akbari, Fateme Jamshidi

Drawbacks of ignoring the causal mechanisms when performing imitation learning have recently been acknowledged. Several approaches both to assess the feasibility of imitation and to circumvent causal confounding and causal misspecifications have been propo ...

Curran Associates, Inc.2023

On Identifiability of Conditional Causal Effects

Seyed Jalal Etesami, Negar Kiyavash, Yaroslav Kivva

We address the problem of identifiability of an arbitrary conditional causal effect given both the causal graph and a set of any observational and/or interventional distributions of the form Q[S] :" P p(S|do(V/S)), where V denotes the set of all observed v ...

2023

A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models

Saber Salehkaleybar, Negar Kiyavash, Ehsan Mokhtarian

We study experiment design for unique identification of the causal graph of a system where the graph may contain cycles. The presence of cycles in the structure introduces major challenges for experiment design as, unlike acyclic graphs, learning the skele ...

2023

Analysis of Large Market Data Using Neural Networks: A Causal Approach

Damir Filipovic, Negar Kiyavash, Marc-Aurèle Antoine Divernois

We develop a data-driven framework to identify the interconnections between firms using an information-theoretic measure. This measure generalizes Granger causality and is capable of detecting nonlinear relationships within a network. Moreover, we develop ...

2023

Learning Bayesian Networks in the Presence of Structural Side Information

Seyed Jalal Etesami, Negar Kiyavash, Sina Akbari, Ehsan Mokhtarian, Fateme Jamshidi

We study the problem of learning a Bayesian network (BN) of a set of variables when structural side information about the system is available. It is well known that learning the structure of a general BN is both computationally and statistically challengin ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2022

Momentum-Based Policy Gradient with Second-Order Information

Patrick Thiran, Saber Salehkaleybar, Negar Kiyavash, Mohammadsadegh Khorasani

Variance-reduced gradient estimators for policy gradient methods have been one of the main focus of research in the reinforcement learning in recent years as they allow acceleration of the estimation process. We propose a variance-reduced policy-gradient m ...

2022

Revisiting the General Identifiability Problem

Seyed Jalal Etesami, Negar Kiyavash, Ehsan Mokhtarian, Yaroslav Kivva

We revisit the problem of general identifiability originally introduced in [Lee et al., 2019] for causal inference and note that it is necessary to add positivity assumption of observational distribution to the original definition of the problem. We show t ...

2022

Minimum Cost Intervention Design for Causal Effect Identification

Seyed Jalal Etesami, Negar Kiyavash, Sina Akbari

Pearl's do calculus is a complete axiomatic approach to learn the identifiable causal effects from observational data. When such an effect is not identifiable, it is necessary to perform a collection of often costly interventions in the system to learn the ...

PMLR2022

A Wasserstein-based measure of conditional dependence

Seyed Jalal Etesami, Negar Kiyavash, Kun Zhang

Measuring conditional dependencies among the variables of a network is of great interest to many disciplines. This paper studies some shortcomings of the existing dependency measures in detecting direct causal influences or their lack of ability for group ...

2022

Causal Effect Identification with Context-specific Independence Relations of Control Variables

Seyed Jalal Etesami, Negar Kiyavash, Ehsan Mokhtarian, Fateme Jamshidi

We study the problem of causal effect identification from observational distribution given the causal graph and some context-specific independence (CSI) relations. It was recently shown that this problem is NP-hard, and while a sound algorithm to learn the ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Patrick Thiran, Saber Salehkaleybar, Negar Kiyavash

We study the performance of Stochastic Cubic Regularized Newton (SCRN) on a class of functions satisfying gradient dominance property with

1\le\alpha\le2

which holds in a wide range of applications in machine learning and signal processing. This conditio ...

NeurIPS2022

The KDD 2021 Workshop on Causal Discovery (CD2021)

Huan Liu, Negar Kiyavash

As a basic and effective tool for explanation, prediction and decision making, causal relationships have been utilized in almost all disciplines. Traditionally, causal relationships are identified by making use of interventions or randomized controlled exp ...

ASSOC COMPUTING MACHINERY2021

Cumulants of Hawkes Processes are Robust to Observation Noise

Patrick Thiran, Matthias Grossglauser, Seyed Jalal Etesami, William Trouleau, Negar Kiyavash

Multivariate Hawkes processes (MHPs) are widely used in a variety of fields to model the occurrence of causally related discrete events in continuous time. Most state-of-the-art approaches address the problem of learning MHPs from perfect traces without no ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

Editorial

Negar Kiyavash, Jia Wang

ASSOC COMPUTING MACHINERY2021

A Variational Inference Approach to Learning Multivariate Wold Processes

Patrick Thiran, Matthias Grossglauser, Seyed Jalal Etesami, William Trouleau, Negar Kiyavash

Temporal point-processes are often used for mathematical modeling of sequences of discrete events with asynchronous timestamps. We focus on a class of temporal point-process models called multivariate Wold processes (MWP). These processes are well suited t ...

MICROTOME PUBLISHING2021

Impact of Data Processing on Fairness in Supervised Learning

Negar Kiyavash

We study the impact of pre and postprocessing for reducing discrimination in data-driven decision makers. We first analyze the fundamental trade-off between fairness and accuracy in a preprocessing approach, and propose a design for a preprocessing module ...

IEEE2021

Optimal Adversarial Policies in the Multiplicative Learning System With a Malicious Expert

Seyed Jalal Etesami, Negar Kiyavash

We consider a learning system based on the conventional multiplicative weight ( MW) rule that combines experts' advice to predict a sequence of true outcomes. It is assumed that one of the experts is malicious and aims to impose the maximum loss on the sys ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems

Negar Kiyavash

Nonconvex minimax problems appear frequently in emerging machine learning applications, such as generative adversarial networks and adversarial learning. Simple algorithms such as the gradient descent ascent (GDA) are the common practice for solving these ...

2020