Ziqi Xu

Orcid: 0000-0003-1748-5801

Affiliations:
  • RMIT University, Melbourne, VIC, Australia
  • University of South Australia (Ph.D.)


According to our database1, Ziqi Xu authored at least 18 papers between 2022 and 2024.

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

Timeline

Legend:

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PhD thesis 
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Links

Online presence:

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Bibliography

2024
Leaning Time-Varying Instruments for Identifying Causal Effects in Time-Series Data.
CoRR, 2024

Deconfounding Time Series Forecasting.
CoRR, 2024

Linking Model Intervention to Causal Interpretation in Model Explanation.
CoRR, 2024

An Item Response Theory-based R Module for Algorithm Portfolio Analysis.
CoRR, 2024

Causal Effect Estimation using identifiable Variational AutoEncoder with Latent Confounders and Post-Treatment Variables.
CoRR, 2024

Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Conditional Instrumental Variable Regression with Representation Learning for Causal Inference.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

TSI: A Multi-view Representation Learning Approach for Time Series Forecasting.
Proceedings of the AI 2024: Advances in Artificial Intelligence, 2024

Instrumental Variable Estimation for Causal Inference in Longitudinal Data with Time-Dependent Latent Confounders.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Causal Effect Estimation with Variational AutoEncoder and the Front Door Criterion.
CoRR, 2023

Linking a predictive model to causal effect estimation.
CoRR, 2023

A Data-Driven Approach to Finding K for K Nearest Neighbor Matching in Average Causal Effect Estimation.
Proceedings of the Web Information Systems Engineering - WISE 2023, 2023

Learning Conditional Instrumental Variable Representation for Causal Effect Estimation.
Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

Disentangled Representation with Causal Constraints for Counterfactual Fairness.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2023

Disentangled Latent Representation Learning for Tackling the Confounding M-Bias Problem in Causal Inference.
Proceedings of the IEEE International Conference on Data Mining, 2023

Disentangled Representation for Causal Mediation Analysis.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

Causal Inference with Conditional Instruments Using Deep Generative Models.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Assessing Classifier Fairness with Collider Bias.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2022


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