← Authors

Ilya Shpitser

31 papers · 2008–2025 · 8 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (8) 🌈 Renaissance Researcher (5) 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge πŸƒ Academic Marathon (17) 🐝 Cross-Pollinator (7) πŸ—ΊοΈ Taxonomy Completionist (11) 🧭 Keyword Pioneer 🐺 Lone Wolf (3) πŸ† Keyword Champion (2) πŸ”¬ Deep Specialist (20) πŸ—ƒοΈ Keyword Collector (91) πŸš€ Conference Pioneer πŸ“ˆ Trend Setter πŸ’Ž Century Club (31) πŸ”₯ Unstoppable (8) ⚑ Prolific Year (5)

Conferences

UAI (11) AISTATS (5) ICML (4) JMLR (4) NIPS (4) CLEAR (1) EMNLP (1) PGM (1)

Top co-authors

Razieh Nabi (6) Rohit Bhattacharya (6) Daniel Malinsky (5) AmirEmad Ghassami (4) Eli Sherman (3) Noam Finkelstein (3) Henrik Von Kleist (2) Zach Wood-Doughty (2) Jaron Lee (2) Sara Cosgrove (1)

Keywords

causal inference (19) graphical model (10) directed acyclic graph (4) causal discovery (4) missing datum (4) markov random field (3) measurement error (3) unmeasured confounding (2) hidden variable (2) semiparametric inference (2) treatment effect (2) causal graphical model (2) constrained optimization (2) structure learning (2) influence function (2) structural equation model (2) social network (2) bayesian network (2) inverse probability weighting (2) causal identification (2)

Papers

Feature Importance Metrics in the Presence of Missing Data ICML 2025
Evaluation of Active Feature Acquisition Methods for Time-varying Feature Settings JMLR 2025
A General Identification Algorithm For Data Fusion Problems Under Systematic Selection UAI 2024
Identification and Estimation for Nonignorable Missing Data: A Data Fusion Approach ICML 2024
Zero Inflation as a Missing Data Problem: a Proxy-based Approach UAI 2024
The Proximal ID Algorithm JMLR 2023
The Lauritzen-Chen Likelihood For Graphical Models AISTATS 2023
Optimal Training of Fair Predictive Models CLEAR 2022
Causal Discovery in Linear Latent Variable Models Subject to Measurement Error NIPS 2022
Semiparametric causal sufficient dimension reduction of multidimensional treatments UAI 2022
Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference AISTATS 2022
Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables JMLR 2022
Differentiable Causal Discovery Under Unmeasured Confounding AISTATS 2021
Partial Identifiability in Discrete Data with Measurement Error UAI 2021
Path dependent structural equation models UAI 2021
Entropic Inequality Constraints from e-separation Relations in Directed Acyclic Graphs with Hidden Variables UAI 2021
General Identification of Dynamic Treatment Regimes Under Interference AISTATS 2020
Full Law Identification in Graphical Models of Missing Data: Completeness Results ICML 2020
Identification and Estimation of Causal Effects Defined by Shift Interventions UAI 2020
Deriving Bounds And Inequality Constraints Using Logical Relations Among Counterfactuals UAI 2020
Intervening on Network Ties UAI 2019
Causal Inference Under Interference And Network Uncertainty UAI 2019
Identification In Missing Data Models Represented By Directed Acyclic Graphs UAI 2019
A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects AISTATS 2019
Learning Optimal Fair Policies ICML 2019
Identification and Estimation of Causal Effects from Dependent Data NIPS 2018
Structure Learning Under Missing Data PGM 2018
Challenges of Using Text Classifiers for Causal Inference EMNLP 2018
Consistent Estimation of Functions of Data Missing Non-Monotonically and Not at Random NIPS 2016
Segregated Graphs and Marginals of Chain Graph Models NIPS 2015
Complete Identification Methods for the Causal Hierarchy JMLR 2008