Ilya Shpitser

Publication Activity (10 Years)

Years Active: 2002-2024
Publications (10 Years): 65

Top Topics

Markov Property

Causal Inference

Top Venues

Publications

Kun Zhang, Ilya Shpitser, Sara Magliacane, Davide Bacciu, Fei Wu, Changshui Zhang, Peter Spirtes
IEEE Transactions on Neural Networks and Learning Systems Special Issue on Causal Discovery and Causality-Inspired Machine Learning. IEEE Trans. Neural Networks Learn. Syst. 35 (4) (2024)
Trung Phung, Jaron J. R. Lee, Opeyemi Oladapo-Shittu, Eili Y. Klein, Ayse Pinar Gurses, Susan M. Hannum, Kimberly Weems, Jill A. Marsteller, Sara E. Cosgrove, Sara C. Keller, Ilya Shpitser
Zero Inflation as a Missing Data Problem: a Proxy-based Approach. CoRR (2024)
Ilya Shpitser
The Lauritzen-Chen Likelihood For Graphical Models. AISTATS (2023)
Henrik von Kleist, Alireza Zamanian, Ilya Shpitser, Narges Ahmidi
Evaluation of Active Feature Acquisition Methods for Static Feature Settings. CoRR (2023)
Henrik von Kleist, Alireza Zamanian, Ilya Shpitser, Narges Ahmidi
Evaluation of Active Feature Acquisition Methods for Time-varying Feature Settings. CoRR (2023)
Ilya Shpitser
When does the ID algorithm fail? CoRR (2023)
Ilya Shpitser, Zach Wood-Doughty, Eric J. Tchetgen Tchetgen
The Proximal ID Algorithm. J. Mach. Learn. Res. 24 (2023)
Zixiao Wang, AmirEmad Ghassami, Ilya Shpitser
Identification and Estimation for Nonignorable Missing Data: A Data Fusion Approach. CoRR (2023)
Jaron J. R. Lee, Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser
Ananke: A Python Package For Causal Inference Using Graphical Models. CoRR (2023)
Jaron J. R. Lee, Gopika Ajaykumar, Ilya Shpitser, Chien-Ming Huang
An Introduction to Causal Inference Methods for Observational Human-Robot Interaction Research. CoRR (2023)
Aurora C. Schmidt, Christopher J. Cameron, Corey Lowman, Joshua Brulé, Amruta Deshpande, Seyyed A. Fatemi, Vladimir Barash, Ariel M. Greenberg, Cash J. Costello, Eli Sherman, Rohit Bhattacharya, Liz McQuillan, Alexander Perrone, Yanni Kouskoulas, Clay Fink, June Zhang, Ilya Shpitser, Michael W. Macy
Searching for explanations: testing social scientific methods in synthetic ground-truthed worlds. Comput. Math. Organ. Theory 29 (1) (2023)
Razieh Nabi, Daniel Malinsky, Ilya Shpitser
Optimal Training of Fair Predictive Models. CLeaR (2022)
Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser
Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables. J. Mach. Learn. Res. 23 (2022)
Yuqin Yang, AmirEmad Ghassami, Mohamed S. Nafea, Negar Kiyavash, Kun Zhang, Ilya Shpitser
Causal Discovery in Linear Latent Variable Models Subject to Measurement Error. CoRR (2022)
Razieh Nabi, Todd McNutt, Ilya Shpitser
Semiparametric causal sufficient dimension reduction of multidimensional treatments. UAI (2022)
Yuqin Yang, AmirEmad Ghassami, Mohamed S. Nafea, Negar Kiyavash, Kun Zhang, Ilya Shpitser
Causal Discovery in Linear Latent Variable Models Subject to Measurement Error. NeurIPS (2022)
AmirEmad Ghassami, Andrew Ying, Ilya Shpitser, Eric Tchetgen Tchetgen
Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference. AISTATS (2022)
Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser, James M. Robins
Causal and counterfactual views of missing data models. CoRR (2022)
Nathan Drenkow, Numair Sani, Ilya Shpitser, Mathias Unberath
Robustness in Deep Learning for Computer Vision: Mind the gap? CoRR (2021)
AmirEmad Ghassami, Ilya Shpitser
Partially Intervenable Causal Models. CoRR (2021)
Guilherme Duarte, Noam Finkelstein, Dean Knox, Jonathan Mummolo, Ilya Shpitser
An Automated Approach to Causal Inference in Discrete Settings. CoRR (2021)
Ranjani Srinivasan, Jaron J. R. Lee, Rohit Bhattacharya, Ilya Shpitser
Path dependent structural equation models. UAI (2021)
Rohit Bhattacharya, Tushar Nagarajan, Daniel Malinsky, Ilya Shpitser
Differentiable Causal Discovery Under Unmeasured Confounding. AISTATS (2021)
Noam Finkelstein, Beata Zjawin, Elie Wolfe, Ilya Shpitser, Robert W. Spekkens
Entropic Inequality Constraints from e-separation Relations in Directed Acyclic Graphs with Hidden Variables. CoRR (2021)
AmirEmad Ghassami, Numair Sani, Yizhen Xu, Ilya Shpitser
Multiply Robust Causal Mediation Analysis with Continuous Treatments. CoRR (2021)
Noam Finkelstein, Roy Adams, Suchi Saria, Ilya Shpitser
Partial Identifiability in Discrete Data with Measurement Error. UAI (2021)
Ilya Shpitser, Zach Wood-Doughty, Eric J. Tchetgen Tchetgen
The Proximal ID Algorithm. CoRR (2021)
Zach Wood-Doughty, Ilya Shpitser, Mark Dredze
Generating Synthetic Text Data to Evaluate Causal Inference Methods. CoRR (2021)
AmirEmad Ghassami, Andrew Ying, Ilya Shpitser, Eric Tchetgen Tchetgen
Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals. CoRR (2021)
Noam Finkelstein, Beata Zjawin, Elie Wolfe, Ilya Shpitser, Robert W. Spekkens
Entropic Inequality Constraints from e-separation Relations in Directed Acyclic Graphs with Hidden Variables. UAI (2021)
Eli Sherman, David Arbour, Ilya Shpitser
General Identification of Dynamic Treatment Regimes Under Interference. CoRR (2020)
Ranjani Srinivasan, Jaron J. R. Lee, Narges Ahmidi, Ilya Shpitser
Path Dependent Structural Equation Models. CoRR (2020)
Numair Sani, Jaron J. R. Lee, Ilya Shpitser
Identification and Estimation of Causal Effects Defined by Shift Interventions. UAI (2020)
Noam Finkelstein, Ilya Shpitser
Deriving Bounds And Inequality Constraints Using Logical Relations Among Counterfactuals. UAI (2020)
Numair Sani, Jaron J. R. Lee, Razieh Nabi, Ilya Shpitser
A Semiparametric Approach to Interpretable Machine Learning. CoRR (2020)
Jaron J. R. Lee, Ilya Shpitser
Identification Methods With Arbitrary Interventional Distributions as Inputs. CoRR (2020)
Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser
Full Law Identification in Graphical Models of Missing Data: Completeness Results. ICML (2020)
Rohit Bhattacharya, Tushar Nagarajan, Daniel Malinsky, Ilya Shpitser
Differentiable Causal Discovery Under Unmeasured Confounding. CoRR (2020)
Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser
Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables. CoRR (2020)
Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser
Full Law Identification In Graphical Models Of Missing Data: Completeness Results. CoRR (2020)
Noam Finkelstein, Roy Adams, Suchi Saria, Ilya Shpitser
Partial Identifiability in Discrete Data With Measurement Error. CoRR (2020)
Numair Sani, Daniel Malinsky, Ilya Shpitser
Explaining The Behavior Of Black-Box Prediction Algorithms With Causal Learning. CoRR (2020)
Eli Sherman, David Arbour, Ilya Shpitser
General Identification of Dynamic Treatment Regimes Under Interference. AISTATS (2020)
Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser, James M. Robins
Identification In Missing Data Models Represented By Directed Acyclic Graphs. UAI (2019)
Rohit Bhattacharya, Daniel Malinsky, Ilya Shpitser
Causal Inference Under Interference And Network Uncertainty. UAI (2019)
Rohit Bhattacharya, Daniel Malinsky, Ilya Shpitser
Causal Inference Under Interference And Network Uncertainty. CoRR (2019)
Razieh Nabi, Daniel Malinsky, Ilya Shpitser
Learning Optimal Fair Policies. ICML (2019)
Eli Sherman, Ilya Shpitser
Intervening on Network Ties. UAI (2019)
Daniel Malinsky, Ilya Shpitser, Thomas S. Richardson
A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects. AISTATS (2019)
Daniel Chicharro, Stefano Panzeri, Ilya Shpitser
Conditionally-additive-noise Models for Structure Learning. CoRR (2019)
Razieh Nabi, Daniel Malinsky, Ilya Shpitser
Optimal Training of Fair Predictive Models. CoRR (2019)
Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser, James M. Robins
Identification In Missing Data Models Represented By Directed Acyclic Graphs. CoRR (2019)
Razieh Nabi, Phyllis Kanki, Ilya Shpitser
Estimation of Personalized Effects Associated With Causal Pathways. CoRR (2018)
Eli Sherman, Ilya Shpitser
Identification and Estimation of Causal Effects from Dependent Data. NeurIPS (2018)
Zach Wood-Doughty, Ilya Shpitser, Mark Dredze
Challenges of Using Text Classifiers for Causal Inference. EMNLP (2018)
Razieh Nabi, Daniel Malinsky, Ilya Shpitser
Learning Optimal Fair Policies. CoRR (2018)
Zach Wood-Doughty, Ilya Shpitser, Mark Dredze
Challenges of Using Text Classifiers for Causal Inference. CoRR (2018)
Ilya Shpitser, Robin J. Evans, Thomas S. Richardson
Acyclic Linear SEMs Obey the Nested Markov Property. UAI (2018)
Ilya Shpitser, Eli Sherman
Identification of Personalized Effects Associated With Causal Pathways. UAI (2018)
Razieh Nabi, Ilya Shpitser
Fair Inference on Outcomes. AAAI (2018)
Alexander Gain, Ilya Shpitser
Structure Learning Under Missing Data. PGM (2018)
Razieh Nabi, Phyllis Kanki, Ilya Shpitser
Estimation of Personalized Effects Associated With Causal Pathways. UAI (2018)
Ilya Shpitser
Consistent Estimation of Functions of Data Missing Non-Monotonically and Not at Random. NIPS (2016)
Ilya Shpitser
Segregated Graphs and Marginals of Chain Graph Models. NIPS (2015)
Ilya Shpitser, Karthika Mohan, Judea Pearl
Missing Data as a Causal and Probabilistic Problem. UAI (2015)
Ilya Shpitser, Robin J. Evans, Thomas S. Richardson, James M. Robins
Sparse Nested Markov models with Log-linear Parameters. CoRR (2013)
Ilya Shpitser
Counterfactual Graphical Models for Longitudinal Mediation Analysis With Unobserved Confounding. Cogn. Sci. 37 (6) (2013)
Tyler J. VanderWeele, Ilya Shpitser
On the definition of a confounder CoRR (2013)
Ilya Shpitser, Robin J. Evans, Thomas S. Richardson, James M. Robins
Sparse Nested Markov models with Log-linear Parameters. UAI (2013)
Ilya Shpitser, Thomas S. Richardson, James M. Robins
An Efficient Algorithm for Computing Interventional Distributions in Latent Variable Causal Models CoRR (2012)
Ilya Shpitser, Tyler J. VanderWeele, James M. Robins
On the Validity of Covariate Adjustment for Estimating Causal Effects CoRR (2012)
Ilya Shpitser, Judea Pearl
Effects of Treatment on the Treated: Identification and Generalization CoRR (2012)
Ilya Shpitser, Judea Pearl
Identification of Conditional Interventional Distributions. CoRR (2012)
Ilya Shpitser, Judea Pearl
What Counterfactuals Can Be Tested CoRR (2012)
Thomas S. Richardson, James M. Robins, Ilya Shpitser
Nested Markov Properties for Acyclic Directed Mixed Graphs. UAI (2012)
Ilya Shpitser, Thomas S. Richardson, James M. Robins
An Efficient Algorithm for Computing Interventional Distributions in Latent Variable Causal Models. UAI (2011)
Eun Yong Kang, Ilya Shpitser, Eleazar Eskin
Respecting Markov Equivalence in Computing Posterior Probabilities of Causal Graphical Features. AAAI (2010)
Ilya Shpitser, Tyler J. VanderWeele, James M. Robins
On the Validity of Covariate Adjustment for Estimating Causal Effects. UAI (2010)
Eun Yong Kang, Chun Ye, Ilya Shpitser, Eleazar Eskin
Detecting the Presence and Absence of Causal Relationships between Expression of Yeast Genes with Very Few Samples. J. Comput. Biol. 17 (3) (2010)
Ilya Shpitser, Judea Pearl
Effects of Treatment on the Treated: Identification and Generalization. UAI (2009)
Ilya Shpitser, Thomas S. Richardson, James M. Robins
Testing Edges by Truncations. IJCAI (2009)
Eun Yong Kang, Ilya Shpitser, Chun Ye, Eleazar Eskin
Detecting the Presence and Absence of Causal Relationships between Expression of Yeast Genes with Very Few Samples. RECOMB (2009)
Ilya Shpitser, Judea Pearl
Complete Identification Methods for the Causal Hierarchy. J. Mach. Learn. Res. 9 (2008)
Ilya Shpitser, Judea Pearl
Dormant Independence. AAAI (2008)
Ilya Shpitser, Judea Pearl
What Counterfactuals Can Be Tested. UAI (2007)
Ilya Shpitser, Judea Pearl
Identification of Conditional Interventional Distributions. UAI (2006)
Ilya Shpitser, Judea Pearl
Identification of Joint Interventional Distributions in Recursive Semi-Markovian Causal Models. AAAI (2006)
Chen Avin, Ilya Shpitser, Judea Pearl
Identifiability of Path-Specific Effects. IJCAI (2005)
Hanna Pasula, Bhaskara Marthi, Brian Milch, Stuart J. Russell, Ilya Shpitser
Identity Uncertainty and Citation Matching. NIPS (2002)