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2020 – today
- 2024
[j7]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): 4899-4901 (2024)- [c43]Zixiao Wang, AmirEmad Ghassami, Ilya Shpitser:
Identification and Estimation for Nonignorable Missing Data: A Data Fusion Approach. ICML 2024 - [i39]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 abs/2406.00549 (2024) - 2023
- [j6]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): 156-187 (2023) - [j5]Ilya Shpitser, Zach Wood-Doughty, Eric J. Tchetgen Tchetgen:
The Proximal ID Algorithm. J. Mach. Learn. Res. 24: 188:1-188:46 (2023) - [c42]Ilya Shpitser:
The Lauritzen-Chen Likelihood For Graphical Models. AISTATS 2023: 4181-4195 - [e2]Robin J. Evans, Ilya Shpitser:
Uncertainty in Artificial Intelligence, UAI 2023, July 31 - 4 August 2023, Pittsburgh, PA, USA. Proceedings of Machine Learning Research 216, PMLR 2023 [contents] - [i38]Jaron J. R. Lee, Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser:
Ananke: A Python Package For Causal Inference Using Graphical Models. CoRR abs/2301.11477 (2023) - [i37]Ilya Shpitser:
When does the ID algorithm fail? CoRR abs/2307.03750 (2023) - [i36]Jaron J. R. Lee, Gopika Ajaykumar, Ilya Shpitser, Chien-Ming Huang:
An Introduction to Causal Inference Methods for Observational Human-Robot Interaction Research. CoRR abs/2310.20468 (2023) - [i35]Zixiao Wang, AmirEmad Ghassami, Ilya Shpitser:
Identification and Estimation for Nonignorable Missing Data: A Data Fusion Approach. CoRR abs/2311.09015 (2023) - [i34]Henrik von Kleist, Alireza Zamanian, Ilya Shpitser, Narges Ahmidi:
Evaluation of Active Feature Acquisition Methods for Time-varying Feature Settings. CoRR abs/2312.01530 (2023) - [i33]Henrik von Kleist, Alireza Zamanian, Ilya Shpitser, Narges Ahmidi:
Evaluation of Active Feature Acquisition Methods for Static Feature Settings. CoRR abs/2312.03619 (2023) - 2022
- [j4]Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser:
Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables. J. Mach. Learn. Res. 23: 295:1-295:76 (2022) - [c41]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: 7210-7239 - [c40]Razieh Nabi, Daniel Malinsky, Ilya Shpitser:
Optimal Training of Fair Predictive Models. CLeaR 2022: 594-617 - [c39]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 - [c38]Razieh Nabi, Todd McNutt, Ilya Shpitser:
Semiparametric causal sufficient dimension reduction of multidimensional treatments. UAI 2022: 1445-1455 - [p2]James M. Robins, Thomas S. Richardson, Ilya Shpitser:
An Interventionist Approach to Mediation Analysis. Probabilistic and Causal Inference 2022: 713-764 - [p1]Ilya Shpitser, Thomas S. Richardson, James M. Robins:
Multivariate Counterfactual Systems and Causal Graphical Models. Probabilistic and Causal Inference 2022: 813-852 - [i32]Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser, James M. Robins:
Causal and counterfactual views of missing data models. CoRR abs/2210.05558 (2022) - [i31]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 abs/2211.03984 (2022) - 2021
- [c37]Rohit Bhattacharya, Tushar Nagarajan, Daniel Malinsky, Ilya Shpitser:
Differentiable Causal Discovery Under Unmeasured Confounding. AISTATS 2021: 2314-2322 - [c36]Ranjani Srinivasan, Jaron J. R. Lee, Rohit Bhattacharya, Ilya Shpitser:
Path dependent structural equation models. UAI 2021: 161-171 - [c35]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: 1045-1055 - [c34]Noam Finkelstein, Roy Adams, Suchi Saria, Ilya Shpitser:
Partial Identifiability in Discrete Data with Measurement Error. UAI 2021: 1798-1808 - [i30]Zach Wood-Doughty, Ilya Shpitser, Mark Dredze:
Generating Synthetic Text Data to Evaluate Causal Inference Methods. CoRR abs/2102.05638 (2021) - [i29]AmirEmad Ghassami, Andrew Ying, Ilya Shpitser, Eric Tchetgen Tchetgen:
Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals. CoRR abs/2104.02929 (2021) - [i28]AmirEmad Ghassami, Numair Sani, Yizhen Xu, Ilya Shpitser:
Multiply Robust Causal Mediation Analysis with Continuous Treatments. CoRR abs/2105.09254 (2021) - [i27]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 abs/2107.07087 (2021) - [i26]Ilya Shpitser, Zach Wood-Doughty, Eric J. Tchetgen Tchetgen:
The Proximal ID Algorithm. CoRR abs/2108.06818 (2021) - [i25]Guilherme Duarte, Noam Finkelstein, Dean Knox, Jonathan Mummolo, Ilya Shpitser:
An Automated Approach to Causal Inference in Discrete Settings. CoRR abs/2109.13471 (2021) - [i24]Nathan Drenkow, Numair Sani, Ilya Shpitser, Mathias Unberath:
Robustness in Deep Learning for Computer Vision: Mind the gap? CoRR abs/2112.00639 (2021) - 2020
- [c33]Eli Sherman, David Arbour, Ilya Shpitser:
General Identification of Dynamic Treatment Regimes Under Interference. AISTATS 2020: 3917-3927 - [c32]Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser:
Full Law Identification in Graphical Models of Missing Data: Completeness Results. ICML 2020: 7153-7163 - [c31]Numair Sani, Jaron J. R. Lee, Ilya Shpitser:
Identification and Estimation of Causal Effects Defined by Shift Interventions. UAI 2020: 949-958 - [c30]Noam Finkelstein, Ilya Shpitser:
Deriving Bounds And Inequality Constraints Using Logical Relations Among Counterfactuals. UAI 2020: 1348-1357 - [i23]Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser:
Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables. CoRR abs/2003.12659 (2020) - [i22]Jaron J. R. Lee, Ilya Shpitser:
Identification Methods With Arbitrary Interventional Distributions as Inputs. CoRR abs/2004.01157 (2020) - [i21]Eli Sherman, David Arbour, Ilya Shpitser:
General Identification of Dynamic Treatment Regimes Under Interference. CoRR abs/2004.01218 (2020) - [i20]Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser:
Full Law Identification In Graphical Models Of Missing Data: Completeness Results. CoRR abs/2004.04872 (2020) - [i19]Numair Sani, Daniel Malinsky, Ilya Shpitser:
Explaining The Behavior Of Black-Box Prediction Algorithms With Causal Learning. CoRR abs/2006.02482 (2020) - [i18]Numair Sani, Jaron J. R. Lee, Razieh Nabi, Ilya Shpitser:
A Semiparametric Approach to Interpretable Machine Learning. CoRR abs/2006.04732 (2020) - [i17]Ranjani Srinivasan, Jaron J. R. Lee, Narges Ahmidi, Ilya Shpitser:
Path Dependent Structural Equation Models. CoRR abs/2008.10706 (2020) - [i16]Rohit Bhattacharya, Tushar Nagarajan, Daniel Malinsky, Ilya Shpitser:
Differentiable Causal Discovery Under Unmeasured Confounding. CoRR abs/2010.06978 (2020) - [i15]Noam Finkelstein, Roy Adams, Suchi Saria, Ilya Shpitser:
Partial Identifiability in Discrete Data With Measurement Error. CoRR abs/2012.12449 (2020)
2010 – 2019
- 2019
- [c29]Daniel Malinsky, Ilya Shpitser, Thomas S. Richardson:
A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects. AISTATS 2019: 3080-3088 - [c28]Razieh Nabi, Daniel Malinsky, Ilya Shpitser:
Learning Optimal Fair Policies. ICML 2019: 4674-4682 - [c27]Eli Sherman, Ilya Shpitser:
Intervening on Network Ties. UAI 2019: 975-984 - [c26]Rohit Bhattacharya, Daniel Malinsky, Ilya Shpitser:
Causal Inference Under Interference And Network Uncertainty. UAI 2019: 1028-1038 - [c25]Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser, James M. Robins:
Identification In Missing Data Models Represented By Directed Acyclic Graphs. UAI 2019: 1149-1158 - [i14]Daniel Chicharro, Stefano Panzeri, Ilya Shpitser:
Conditionally-additive-noise Models for Structure Learning. CoRR abs/1905.08360 (2019) - [i13]Rohit Bhattacharya, Daniel Malinsky, Ilya Shpitser:
Causal Inference Under Interference And Network Uncertainty. CoRR abs/1907.00221 (2019) - [i12]Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser, James M. Robins:
Identification In Missing Data Models Represented By Directed Acyclic Graphs. CoRR abs/1907.00241 (2019) - [i11]Razieh Nabi, Daniel Malinsky, Ilya Shpitser:
Optimal Training of Fair Predictive Models. CoRR abs/1910.04109 (2019) - 2018
- [c24]Razieh Nabi, Ilya Shpitser:
Fair Inference on Outcomes. AAAI 2018: 1931-1940 - [c23]Zach Wood-Doughty, Ilya Shpitser, Mark Dredze:
Challenges of Using Text Classifiers for Causal Inference. EMNLP 2018: 4586-4598 - [c22]Eli Sherman, Ilya Shpitser:
Identification and Estimation of Causal Effects from Dependent Data. NeurIPS 2018: 9446-9457 - [c21]Alexander Gain, Ilya Shpitser:
Structure Learning Under Missing Data. PGM 2018: 121-132 - [c20]Ilya Shpitser, Eli Sherman:
Identification of Personalized Effects Associated With Causal Pathways. UAI 2018: 530-539 - [c19]Razieh Nabi, Phyllis Kanki, Ilya Shpitser:
Estimation of Personalized Effects Associated With Causal Pathways. UAI 2018: 673-682 - [c18]Ilya Shpitser, Robin J. Evans, Thomas S. Richardson:
Acyclic Linear SEMs Obey the Nested Markov Property. UAI 2018: 735-745 - [i10]Razieh Nabi, Daniel Malinsky, Ilya Shpitser:
Learning Optimal Fair Policies. CoRR abs/1809.02244 (2018) - [i9]Razieh Nabi, Phyllis Kanki, Ilya Shpitser:
Estimation of Personalized Effects Associated With Causal Pathways. CoRR abs/1809.10791 (2018) - [i8]Zach Wood-Doughty, Ilya Shpitser, Mark Dredze:
Challenges of Using Text Classifiers for Causal Inference. CoRR abs/1810.00956 (2018) - 2016
- [c17]Ilya Shpitser:
Consistent Estimation of Functions of Data Missing Non-Monotonically and Not at Random. NIPS 2016: 3144-3152 - 2015
- [c16]Ilya Shpitser:
Segregated Graphs and Marginals of Chain Graph Models. NIPS 2015: 1720-1728 - [c15]Ilya Shpitser, Karthika Mohan, Judea Pearl:
Missing Data as a Causal and Probabilistic Problem. UAI 2015: 802-811 - [e1]Ricardo Silva, Ilya Shpitser, Robin J. Evans, Jonas Peters, Tom Claassen:
Proceedings of the UAI 2015 Workshop on Advances in Causal Inference co-located with the 31st Conference on Uncertainty in Artificial Intelligence (UAI 2015), Amsterdam, The Netherlands, July 16, 2015. CEUR Workshop Proceedings 1504, CEUR-WS.org 2015 [contents] - 2013
- [j3]Ilya Shpitser:
Counterfactual Graphical Models for Longitudinal Mediation Analysis With Unobserved Confounding. Cogn. Sci. 37(6): 1011-1035 (2013) - [c14]Ilya Shpitser, Robin J. Evans, Thomas S. Richardson, James M. Robins:
Sparse Nested Markov models with Log-linear Parameters. UAI 2013 - [i7]Tyler J. VanderWeele, Ilya Shpitser:
On the definition of a confounder. CoRR abs/1304.0564 (2013) - [i6]Ilya Shpitser, Robin J. Evans, Thomas S. Richardson, James M. Robins:
Sparse Nested Markov models with Log-linear Parameters. CoRR abs/1309.6863 (2013) - 2012
- [c13]Thomas S. Richardson, James M. Robins, Ilya Shpitser:
Nested Markov Properties for Acyclic Directed Mixed Graphs. UAI 2012: 13 - [i5]Ilya Shpitser, Thomas S. Richardson, James M. Robins:
An Efficient Algorithm for Computing Interventional Distributions in Latent Variable Causal Models. CoRR abs/1202.3763 (2012) - [i4]Ilya Shpitser, Tyler J. VanderWeele, James M. Robins:
On the Validity of Covariate Adjustment for Estimating Causal Effects. CoRR abs/1203.3515 (2012) - [i3]Ilya Shpitser, Judea Pearl:
Effects of Treatment on the Treated: Identification and Generalization. CoRR abs/1205.2615 (2012) - [i2]Ilya Shpitser, Judea Pearl:
What Counterfactuals Can Be Tested. CoRR abs/1206.5294 (2012) - [i1]Ilya Shpitser, Judea Pearl:
Identification of Conditional Interventional Distributions. CoRR abs/1206.6876 (2012) - 2011
- [c12]Ilya Shpitser, Thomas S. Richardson, James M. Robins:
An Efficient Algorithm for Computing Interventional Distributions in Latent Variable Causal Models. UAI 2011: 661-670 - 2010
- [j2]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): 533-546 (2010) - [c11]Eun Yong Kang, Ilya Shpitser, Eleazar Eskin:
Respecting Markov Equivalence in Computing Posterior Probabilities of Causal Graphical Features. AAAI 2010: 1175-1180 - [c10]Ilya Shpitser, Tyler J. VanderWeele, James M. Robins:
On the Validity of Covariate Adjustment for Estimating Causal Effects. UAI 2010: 527-536
2000 – 2009
- 2009
- [c9]Ilya Shpitser, Thomas S. Richardson, James M. Robins:
Testing Edges by Truncations. IJCAI 2009: 1957-1963 - [c8]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: 466-481 - [c7]Ilya Shpitser, Judea Pearl:
Effects of Treatment on the Treated: Identification and Generalization. UAI 2009: 514-521 - 2008
- [j1]Ilya Shpitser, Judea Pearl:
Complete Identification Methods for the Causal Hierarchy. J. Mach. Learn. Res. 9: 1941-1979 (2008) - [c6]Ilya Shpitser, Judea Pearl:
Dormant Independence. AAAI 2008: 1081-1087 - 2007
- [c5]Ilya Shpitser, Judea Pearl:
What Counterfactuals Can Be Tested. UAI 2007: 352-359 - 2006
- [c4]Ilya Shpitser, Judea Pearl:
Identification of Joint Interventional Distributions in Recursive Semi-Markovian Causal Models. AAAI 2006: 1219-1226 - [c3]Ilya Shpitser, Judea Pearl:
Identification of Conditional Interventional Distributions. UAI 2006 - 2005
- [c2]Chen Avin, Ilya Shpitser, Judea Pearl:
Identifiability of Path-Specific Effects. IJCAI 2005: 357-363 - 2002
- [c1]Hanna Pasula, Bhaskara Marthi, Brian Milch, Stuart Russell, Ilya Shpitser:
Identity Uncertainty and Citation Matching. NIPS 2002: 1401-1408
Coauthor Index
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