A Recursive Markov Blanket-Based Approach to Causal Structure Learning. - Ehsan Mokhtarian; Sina Akbari; AmirEmad Ghassami; Negar Kiyavash (CoRR, 2020)

A Recursive Markov Boundary-Based Approach to Causal Structure Learning. - Ehsan Mokhtarian; Sina Akbari; AmirEmad Ghassami; Negar Kiyavash (CD@KDD, 2021)

Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias. - Sina Akbari; Ehsan Mokhtarian; AmirEmad Ghassami; Negar Kiyavash (NeurIPS, 2021)

Lower Bounds on Kemeny Rank Aggregation with Non-Strict Rankings. - Sina Akbari; Adolfo R. Escobedo (SSCI, 2021)

Learning Bayesian Networks in the Presence of Structural Side Information. - Ehsan Mokhtarian; Sina Akbari; Fateme Jamshidi; Jalal Etesami; Negar Kiyavash (AAAI, 2022)

Minimum Cost Intervention Design for Causal Effect Identification. - Sina Akbari; Jalal Etesami; Negar Kiyavash (ICML, 2022)

Top-k List Aggregation: Mathematical Formulations and Polyhedral Comparisons. - Sina Akbari; Adolfo R. Escobedo (ISCO, 2022)

A Free Lunch with Influence Functions? Improving Neural Network Estimates with Concepts from Semiparametric Statistics. - Matthew J. Vowels; Sina Akbari; Jalal Etesami; Necati Cihan Camgöz; Richard Bowden (CoRR, 2022)

Causal Discovery in Probabilistic Networks with an Identifiable Causal Effect. - Sina Akbari; Fateme Jamshidi; Ehsan Mokhtarian; Matthew J. Vowels; Jalal Etesami; Negar Kiyavash (CoRR, 2022)

Causal Effect Identification in Uncertain Causal Networks. - Sina Akbari; Fateme Jamshidi; Ehsan Mokhtarian; Matthew J. Vowels; Jalal Etesami; Negar Kiyavash (NeurIPS, 2023)

Causal Imitability Under Context-Specific Independence Relations. - Fateme Jamshidi; Sina Akbari; Negar Kiyavash (NeurIPS, 2023)

Learning Causal Graphs via Monotone Triangular Transport Maps. - Sina Akbari; Luca Ganassali; Negar Kiyavash (CoRR, 2023)

A Free Lunch with Influence Functions? An Empirical Evaluation of Influence Functions for Average Treatment Effect Estimation. - Matthew James Vowels; Sina Akbari; Necati Cihan Camgöz; Richard Bowden (Trans. Mach. Learn. Res., 2023)

Approximate Condorcet Partitioning: Solving large-scale rank aggregation problems. - Sina Akbari; Adolfo R. Escobedo (Comput. Oper. Res., 2023)

Triple Changes Estimator for Targeted Policies. - Sina Akbari; Negar Kiyavash (ICML, 2024)

Fast Proxy Experiment Design for Causal Effect Identification. - Sepehr Elahi; Sina Akbari; Jalal Etesami; Negar Kiyavash; Patrick Thiran (NeurIPS, 2024)

Recursive Causal Discovery. - Ehsan Mokhtarian; Sepehr Elahi; Sina Akbari; Negar Kiyavash (CoRR, 2024)

CaTs and DAGs: Integrating Directed Acyclic Graphs with Transformers and Fully-Connected Neural Networks for Causally Constrained Predictions. - Matthew J. Vowels; Mathieu Rochat; Sina Akbari (CoRR, 2024)

Multi-armed Bandits with Missing Outcome. - Ilia Mahrooghi; Mahshad Moradi; Sina Akbari; Negar Kiyavash (CoRR, 2024)

Sample Complexity of Nonparametric Closeness Testing for Continuous Distributions and Its Application to Causal Discovery with Hidden Confounding. - Fateme Jamshidi; Sina Akbari; Negar Kiyavash (CLeaR, 2025)

Causal Effect Identification in Heterogeneous Environments from Higher-Order Moments. - Yaroslav Kivva; Sina Akbari; Saber Salehkaleybar; Negar Kiyavash (UAI, 2025)

Multi-armed Bandits with Missing Outcomes. - Ilia Mahrooghi; Mahshad Moradi; Sina Akbari; Negar Kiyavash (UAI, 2025)