Charles K. Assaad

Orcid: 0000-0003-3571-3636

According to our database1, Charles K. Assaad authored at least 21 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithms.
Trans. Mach. Learn. Res., 2024

Causal reasoning in difference graphs.
CoRR, 2024

Average Controlled and Average Natural Micro Direct Effects in Summary Causal Graphs.
CoRR, 2024

Identifying macro conditional independencies and macro total effects in summary causal graphs with latent confounding.
CoRR, 2024

Toward identifiability of total effects in summary causal graphs with latent confounders: an extension of the front-door criterion.
CoRR, 2024

On the Fly Detection of Root Causes from Observed Data with Application to IT Systems.
CoRR, 2024

On the Fly Detection of Root Causes from Observed Data with Application to IT Systems.
Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

Identifiability of Direct Effects from Summary Causal Graphs.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Identifiability of total effects from abstractions of time series causal graphs.
CoRR, 2023

Case Studies of Causal Discovery from IT Monitoring Time Series.
CoRR, 2023

Hybrids of Constraint-based and Noise-based Algorithms for Causal Discovery from Time Series.
CoRR, 2023

Survey and Evaluation of Causal Discovery Methods for Time Series (Extended Abstract).
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with Loops.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Survey and Evaluation of Causal Discovery Methods for Time Series.
J. Artif. Intell. Res., 2022

A Conditional Mutual Information Estimator for Mixed Data and an Associated Conditional Independence Test.
Entropy, 2022

Entropy-Based Discovery of Summary Causal Graphs in Time Series.
Entropy, 2022

Inferring extended summary causal graphs from observational time series.
CoRR, 2022

Discovery of extended summary graphs in time series.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

2021
Causal Discovery between time series. (Découvertes de relations causales entreséries temporelles).
PhD thesis, 2021

A Mixed Noise and Constraint-Based Approach to Causal Inference in Time Series.
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

2019
Scaling Causal Inference in Additive Noise Models.
Proceedings of the 2019 ACM SIGKDD Workshop on Causal Discovery, 2019


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