4 citations · 9 across the 15 of their papers we have counts for
16 papers
The Bidirectionality-Preserving Feedback Arc Set Problem with an Application to Faculty Hiring Network Analysis
Sina Akbari, Adolfo R. Escobedo, Jorge A. Sefair
This work introduces a novel combinatorial optimization problem to uncover strict and non-strict hierarchical structures underlying digraphs with bidirected arcs. We show that the…
Causal Effect Identification in Heterogeneous Environments from Higher-Order Moments
Yaroslav Kivva, Sina Akbari, Saber Salehkaleybar +1
We investigate the estimation of the causal effect of a treatment variable on an outcome in the presence of a latent confounder. We first show that the causal effect is identifiabl…
Sample Complexity of Nonparametric Closeness Testing for Continuous Distributions and Its Application to Causal Discovery with Hidden Confounding
Fateme Jamshidi, Sina Akbari, Negar Kiyavash
We study the problem of closeness testing for continuous distributions and its implications for causal discovery. Specifically, we analyze the sample complexity of distinguishing w…
Semiparametric Triple Difference Estimators
Sina Akbari, Negar Kiyavash, AmirEmad Ghassami
The triple difference causal inference framework is an extension of the well-known difference-in-differences framework. It relaxes the parallel trends assumption of the difference-…
Multi-armed Bandits with Missing Outcome
Ilia Mahrooghi, Mahshad Moradi, Sina Akbari +1
While significant progress has been made in designing algorithms that minimize regret in online decision-making, real-world scenarios often introduce additional complexities, perha…
Fast Proxy Experiment Design for Causal Effect Identification
Sepehr Elahi, Sina Akbari, Jalal Etesami +2
Identifying causal effects is a key problem of interest across many disciplines. The two long-standing approaches to estimate causal effects are observational and experimental (ran…
Recursive Causal Discovery
Ehsan Mokhtarian, Sepehr Elahi, Sina Akbari +1
Causal discovery, i.e., learning the causal graph from data, is often the first step toward the identification and estimation of causal effects, a key requirement in numerous scien…
Non-linear Triple Changes Estimator for Targeted Policies
Sina Akbari, Negar Kiyavash
The renowned difference-in-differences (DiD) estimator relies on the assumption of 'parallel trends,' which does not hold in many practical applications. To address this issue, the…
Causal Imitability Under Context-Specific Independence Relations
Fateme Jamshidi, Sina Akbari, Negar Kiyavash
Drawbacks of ignoring the causal mechanisms when performing imitation learning have recently been acknowledged. Several approaches both to assess the feasibility of imitation and t…
Learning Causal Graphs via Monotone Triangular Transport Maps
Sina Akbari, Luca Ganassali, Negar Kiyavash
We study the problem of causal structure learning from data using optimal transport (OT). Specifically, we first provide a constraint-based method which builds upon lower-triangula…
Causal Effect Identification in Uncertain Causal Networks
Sina Akbari, Fateme Jamshidi, Ehsan Mokhtarian +3
Causal identification is at the core of the causal inference literature, where complete algorithms have been proposed to identify causal queries of interest. The validity of these…
Experimental Design for Causal Effect Identification
Sina Akbari, Jalal Etesami, Negar Kiyavash
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 p…
A Free Lunch with Influence Functions? Improving Neural Network Estimates with Concepts from Semiparametric Statistics
Matthew J. Vowels, Sina Akbari, Necati Cihan Camgoz +1
Parameter estimation in empirical fields is usually undertaken using parametric models, and such models readily facilitate statistical inference. Unfortunately, they are unlikely t…
Learning Bayesian Networks in the Presence of Structural Side Information
Ehsan Mokhtarian, Sina Akbari, Fateme Jamshidi +2
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 st…
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…
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…