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

Negar Kiyavash, Yaroslav Kivva, Seyed Jalal Etesami

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

Negar Kiyavash, Ehsan Mokhtarian, Saber Salehkaleybar

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

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

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, Negar Kiyavash, Mohammadsadegh Khorasani, Saber Salehkaleybar

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

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

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

Negar Kiyavash, Sina Akbari, Seyed Jalal Etesami

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