Ilya Shpitser

Orcid: 0000-0003-2571-7326

According to our database1, Ilya Shpitser authored at least 68 papers between 2002 and 2024.

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Bibliography

2024
IEEE Transactions on Neural Networks and Learning Systems Special Issue on Causal Discovery and Causality-Inspired Machine Learning.
IEEE Trans. Neural Networks Learn. Syst., April, 2024

2023
Searching for explanations: testing social scientific methods in synthetic ground-truthed worlds.
Comput. Math. Organ. Theory, March, 2023

The Proximal ID Algorithm.
J. Mach. Learn. Res., 2023

Evaluation of Active Feature Acquisition Methods for Static Feature Settings.
CoRR, 2023

Evaluation of Active Feature Acquisition Methods for Time-varying Feature Settings.
CoRR, 2023

Identification and Estimation for Nonignorable Missing Data: A Data Fusion Approach.
CoRR, 2023

An Introduction to Causal Inference Methods for Observational Human-Robot Interaction Research.
CoRR, 2023

When does the ID algorithm fail?
CoRR, 2023

Ananke: A Python Package For Causal Inference Using Graphical Models.
CoRR, 2023

The Lauritzen-Chen Likelihood For Graphical Models.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables.
J. Mach. Learn. Res., 2022

Causal and counterfactual views of missing data models.
CoRR, 2022

Semiparametric causal sufficient dimension reduction of multidimensional treatments.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

Causal Discovery in Linear Latent Variable Models Subject to Measurement Error.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Optimal Training of Fair Predictive Models.
Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Multivariate Counterfactual Systems and Causal Graphical Models.
Proceedings of the Probabilistic and Causal Inference: The Works of Judea Pearl, 2022

An Interventionist Approach to Mediation Analysis.
Proceedings of the Probabilistic and Causal Inference: The Works of Judea Pearl, 2022

2021
Robustness in Deep Learning for Computer Vision: Mind the gap?
CoRR, 2021

An Automated Approach to Causal Inference in Discrete Settings.
CoRR, 2021

Multiply Robust Causal Mediation Analysis with Continuous Treatments.
CoRR, 2021

Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals.
CoRR, 2021

Generating Synthetic Text Data to Evaluate Causal Inference Methods.
CoRR, 2021

Path dependent structural equation models.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Entropic Inequality Constraints from e-separation Relations in Directed Acyclic Graphs with Hidden Variables.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Partial Identifiability in Discrete Data with Measurement Error.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Differentiable Causal Discovery Under Unmeasured Confounding.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Path Dependent Structural Equation Models.
CoRR, 2020

A Semiparametric Approach to Interpretable Machine Learning.
CoRR, 2020

Explaining The Behavior Of Black-Box Prediction Algorithms With Causal Learning.
CoRR, 2020

Identification Methods With Arbitrary Interventional Distributions as Inputs.
CoRR, 2020

Identification and Estimation of Causal Effects Defined by Shift Interventions.
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Deriving Bounds And Inequality Constraints Using Logical Relations Among Counterfactuals.
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Full Law Identification in Graphical Models of Missing Data: Completeness Results.
Proceedings of the 37th International Conference on Machine Learning, 2020

General Identification of Dynamic Treatment Regimes Under Interference.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Conditionally-additive-noise Models for Structure Learning.
CoRR, 2019

Intervening on Network Ties.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Identification In Missing Data Models Represented By Directed Acyclic Graphs.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Causal Inference Under Interference And Network Uncertainty.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Learning Optimal Fair Policies.
Proceedings of the 36th International Conference on Machine Learning, 2019

A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
Identification of Personalized Effects Associated With Causal Pathways.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Acyclic Linear SEMs Obey the Nested Markov Property.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Estimation of Personalized Effects Associated With Causal Pathways.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Structure Learning Under Missing Data.
Proceedings of the International Conference on Probabilistic Graphical Models, 2018

Identification and Estimation of Causal Effects from Dependent Data.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Challenges of Using Text Classifiers for Causal Inference.
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31, 2018

Fair Inference on Outcomes.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2016
Consistent Estimation of Functions of Data Missing Non-Monotonically and Not at Random.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015
Missing Data as a Causal and Probabilistic Problem.
Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Segregated Graphs and Marginals of Chain Graph Models.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2013
On the definition of a confounder
CoRR, 2013

Counterfactual Graphical Models for Longitudinal Mediation Analysis With Unobserved Confounding.
Cogn. Sci., 2013

Sparse Nested Markov models with Log-linear Parameters.
Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

2012
Nested Markov Properties for Acyclic Directed Mixed Graphs.
Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

2011
An Efficient Algorithm for Computing Interventional Distributions in Latent Variable Causal Models.
Proceedings of the UAI 2011, 2011

2010
Detecting the Presence and Absence of Causal Relationships between Expression of Yeast Genes with Very Few Samples.
J. Comput. Biol., 2010

On the Validity of Covariate Adjustment for Estimating Causal Effects.
Proceedings of the UAI 2010, 2010

Respecting Markov Equivalence in Computing Posterior Probabilities of Causal Graphical Features.
Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, 2010

2009
Effects of Treatment on the Treated: Identification and Generalization.
Proceedings of the UAI 2009, 2009

Testing Edges by Truncations.
Proceedings of the IJCAI 2009, 2009

2008
Complete Identification Methods for the Causal Hierarchy.
J. Mach. Learn. Res., 2008

Dormant Independence.
Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, 2008

2007
What Counterfactuals Can Be Tested.
Proceedings of the UAI 2007, 2007

2006
Identification of Conditional Interventional Distributions.
Proceedings of the UAI '06, 2006

Identification of Joint Interventional Distributions in Recursive Semi-Markovian Causal Models.
Proceedings of the Proceedings, 2006

2005
Identifiability of Path-Specific Effects.
Proceedings of the IJCAI-05, Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, Edinburgh, Scotland, UK, July 30, 2005

2002
Identity Uncertainty and Citation Matching.
Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002


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