Daniel Malinsky

Rohit Bhattacharya

Proceedings of the Uncertainty in Artificial Intelligence, 2023

2022

Optimal Training of Fair Predictive Models.

[BibT_eX]

[DOI]

Razieh Nabi

Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

2021

Differentiable Causal Discovery Under Unmeasured Confounding.

[BibT_eX]

[DOI]

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Explaining The Behavior Of Black-Box Prediction Algorithms With Causal Learning.

[BibT_eX]

[DOI]

Numair Sani

CoRR, 2020

2019

Causal Inference Under Interference And Network Uncertainty.

[BibT_eX]

[DOI]

Rohit Bhattacharya

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Learning Optimal Fair Policies.

[BibT_eX]

[DOI]

Razieh Nabi

Proceedings of the 36th International Conference on Machine Learning, 2019

Learning the Structure of a Nonstationary Vector Autoregression.

[BibT_eX]

[DOI]

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects.

[BibT_eX]

[DOI]

Thomas S. Richardson

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Intervening on structure.

[BibT_eX]

[DOI]

Synth., 2018

Causal Learning for Partially Observed Stochastic Dynamical Systems.

[BibT_eX]

[DOI]

Søren Wengel Mogensen

Niels Richard Hansen

Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Causal Structure Learning from Multivariate Time Series in Settings with Unmeasured Confounding.

[BibT_eX]

[DOI]

Proceedings of 2018 ACM SIGKDD Workshop on Causal Discovery, 2018

2017

Estimating bounds on causal effects in high-dimensional and possibly confounded systems.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2017

2016

Estimating Causal Effects with Ancestral Graph Markov Models.

[BibT_eX]

[DOI]