- Office: Malone Hall 321.
- Email: first name concatenated with the first letter of last name, all lowercase, at cs.jhu.edu
I am a John C. Malone Associate Professor in the Department of Computer Science in the Whiting School of Engineering at the Johns Hopkins University. I am a member of the Malone Center For Engineering in Healthcare. I graduated from UCLA where I had great privilege to be advised by Judea Pearl. As a postdoctoral scholar at the Harvard School of Public Health, I was fortunate to receive mentorship from James M. Robins.
Research Interests:
Causal inference, missing data, graphical models, algorithmic fairness, statistical inference in semi-parametric models.Teaching:
Spring 2024: Disinformation Self-defense (600.277).
Fall 2023: Causal Inference (601.477/677).
Fall 2024: Introduction To Machine Learning (601.475/675).
Students and Postdocs:
Ph.D. Students:
- Trung Phang
- Helen Guo
- Jacob Chen
Alumni:
- Numair Sani (starting a data science consulting company in India).
- Jaron Lee (joining Datavant as a data scientist).
- Amir Emad Ghassami (joining the Department of Mathematics and Statistics at Boston University).
- Ranjani Srinivasan (joining Boston Consulting Group as a consultant).
- Eli Sherman (joining Credo as a data scientist).
- Noam Finkelstein (joining Weill Cornell Medicine as a postdoctoral scholar under the supervision of Prof. Ashley Laughney).
- Dan Malinsky (joining the Department of Biostatistics at Columbia University as Assistant Professor).
- Razieh Nabi (joining the Department of Biostatistics at Emory University as Rollins Assistant Professor).
- Rohit Bhattacharya (joining the Department of Computer Science at Williams College as Assistant Professor).
News:
6/11/2024. Congratulations to my colleague Jaron Lee, who has successfully defended his dissertation and will be joining Datavant as a data scientist!
3/9/2023. Congratulations to my colleague Amir Ghassami, who will be starting as a tenure track Assistant Professor at the Department of Mathematics and Statistics at Boston University!
9/2/2022. Congratulations to my colleague Eli Sherman, who has successfully defended his dissertation and will be joining Credo as a data scientist!
1/20/2022. Congratulations to my colleague Noam Finkelstein who will be starting as a postdoctoral scholar at Weill Cornell Medicine under the supervision of Prof. Ashley Laughney.
3/20/2021. Congratulations to my colleague Razieh Nabi who will be starting as a tenure track Rollins Assistant Professor at the Department of Biostatistics at Emory University!
3/20/2021. Congratulations to my colleague Rohit Bhattacharya who will be starting as a tenure track Assistant Professor at the Department of Computer Science at Williams College!
1/28/2021. Congratulations to Noam Finkelstein for winning a fellowship from the Mathematical Institute for Data Science (MINDS).
6/1/2020. Congratulations to Eli Sherman for receiving the prestigious Google PhD Fellowship Award!
5/1/2020. I am pleased to announce the public release of Ananke, a causal inference package written in Python, authored by Rohit Bhattacharya, Jaron Lee, and Razieh Nabi. Links to documentation, gitlab, and the python package index.
3/5/2020. Congratulations to my colleague Daniel Malinsky who will be starting as a tenure track Assistant Professor at the Department of Biostatistics at Columbia University!
2/14/2020. I have received the NSF Faculty Early Career Development (CAREER) award, for my project "Robust Causal And Statistical Inference in High Dimensional Structured Systems with Hidden Variables."
1/17/2020. The intersession course on critical thinking and causal inference that Razieh Nabi and Rohit Bhattacharya developed and taught during the Winter Break of 2020 has been featured in the Hub magazine!
12/10/2019. Congratulations to Eli Sherman for winning a fellowship from the Mathematical Institute for Data Science (MINDS).
5/24/2019. Congratulations to Rohit Bhattacharya and Razieh Nabi for winning the Thomas R. Ten Have Award at the Atlantic Causal Inference Conference 2019 held in Montreal in 2019, for their poster titled "Identification In Missing Data Models Represented By Directed Acyclic Graphs".
4/25/2017. I have received the Causality in Statistics Education Award from the American Statistical Association, for developing my course in causal inference at Johns Hopkins Computer Science: CS 477-677.
Recent Tutorials:
"Mediation Analysis: Old and New." A full day tutorial at the 36th New England Statistics Symposium (co-taught with Judith Lok). June 4th, 2023.
"Graphical Model Identification Theory For Causal Inference and Missing Data Problems." A full day tutorial at the Atlantic Causal Inference Conference 2019, (co-taught with James M. Robins). May 22, 2019.
"Causal Fairness Criteria: Algorithms and Open Problems" A half-day continuing education course at the Joint Statistical Meetings 2022, (co-taught with Razieh Nabi and Dan Malinsky). August 7, 2022.Selected Recent Talks:
"Causal Inference with Hidden Mediators." NSF Fairness in AI Principal Investigator Meeting. Arlington, VA. January 9th, 2024.
"Fairness By Causal Mediation Analysis: Criteria, Algorithms, and Open Problems." Invited talk at the "Ethical Considerations in Health Data Science" workshop, Royal Statistical Society, London. Dec. 13, 2023.
"General methods for identification and estimation in hidden variable causal systems." University of Pennsylvania Center for Causal Inference Distinguished Faculty Seminar. Philadelphia, PA. November 16, 2023.
"The Proximal ID Algorithm." Invited talk at the Pacific Causal Inference Conference. Beijing, China. September 17, 2023.
"An Introduction to Causal Inference." Invited talk at the Causality in Ecology Workshop. Johns Hopkins University, Baltimore. August 21, 2023.
"Introduction to Proximal Causal Inference Part I: The Proximal ID Algorithm." Keynote talk at the FunCausal 2023: Fundamental Challenges on Causality colloquium, Grenoble France. May 10, 2023.
"An Introduction to Causal Inference." At the Causal Inference and Quantum Foundations Workshop. The Perimeter Institute, Waterloo, Canada. April 17, 2023.
"Fairness By Causal Mediation Analysis: Criteria, Algorithms, and Open Problems." Invited talk at the Online Causal Inference Seminar. January 10, 2023.
"Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables." Adobe Causal Inference Workshop. October 17, 2022.
"Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables." Innovative Development of Semiparametrics for Heterogeneous Causal Effects in Epidemiology — Invited Papers (Joint Statistical Meetings 2022). August 8, 2022.
"Coexisting With Potential Responses." Invited talk at the Rutgers Foundations of Probability Seminar.
"Satisfying Causal Fairness Criteria: Algorithms, and Open Problems." Invited talk at the Statistics Annual Winter Workshop 2022: Algorithm Fairness and Bias in AI, at the University of Florida.
"The Potential Outcomes Calculus." Invited session talk at the Society for Epidemiologic Research (SER) 2021 Annual Meeting. June 24, 2021.
"Fairness By Causal Mediation Analysis: Criteria, Algorithms, and Open Problems." Invited talk at the ENAR 2021 Spring Meeting. March 16, 2021.
"Identification and Estimation of Causal Parameters via a Modified Factorization of a Graphical Model." Invited talk at the virtual opening workshop of the SAMSI Data Science in the Social and Behavioral Sciences program. January 11, 2021.
"Understanding mechanisms behind COVID-19 disparities." At the Johns Hopkins Research Symposium on Engineering in Healthcare: Disparities and COVID-19. January 11, 2021.
"Identification and Estimation of Causal Parameters via a Modified Factorization of a Graphical Model." Invited talk at the Pacific Causal Inference Conference. September 27, 2020.
"Causal Inference Under Interference and Network Uncertainty." Keynote talk at the KDD Causal Discovery Workshop. August 24, 2020.
"Identification And Estimation In Graphical Models Of Missing Data." The Online Causal Inference Seminar, May 12, 2020.
"Fairness By Causal Mediation Analysis: Criteria, Algorithms, and Open Problems." Columbia Biostatistics Colloquium. January 30, 2020.
"Identification and Estimation of Causal Parameters via a Modified Factorization of a Graphical Model." Invited talk at the seminar for the Wharton Statistics Department at the University of Pennsylvania.
"Causal Inference And Applications In Public Health And Radiation Oncology." Keynote talk at the International Conference on the Use of Computers in Radiation Therapy and the International Conference on Monte Carlo Techniques for Medical Applications (ICCR-MCMA), in Montreal, Canada. June 18, 2019.
"Fairness By Causal Mediation Analysis: Criteria, Algorithms, and Open Problems." Keynote talk at the 10th Workshop in Decisions, Games and Logic: "Ethics, Statistics, and Fair AI", in Pasadena, California. June 10, 2019.
"Uncertainty and Networks." At the "Causality vs Prediction" program review, in Boston, Massachusetts. June 6, 2019.
"Identification And Estimation Via A Modified Factorization Of A Graphical Model." Invited talk at the workshop "Foundations and New Horizons for Causal Inference," in Oberwolfach, Germany. May 29, 2019.
"Estimation Of Personalized Effects Associated With Causal Pathways." At the 33rd New England Statistics Symposium (NESS), in Hartford, Connecticut. May 16, 2019.
"The Potential Outcomes Calculus." Invited talk at the IRSA (Institute for Research on Statistics and its Applications) Conference: Causal Inference and Data Science, in Minneapolis, Minnesota. May 3, 2019.
"Fair Inference On Outcomes." Invited talk at the Harvard applied statistics workshop, in Boston, Massachusetts. February 13, 2019.
"Estimation Of Personalized Effects Associated With Causal Pathways." Invited talk at the workshop "Models and Machine Learning for Causal Inference and Decision Making in Health Research", in Providence, Rhode Island. January 16, 2019.
Publications:
Google ScholarRecent Book Chapters:
"Multivariate Counterfactual Systems and Causal Graphical Models," with Thomas S. Richardson, and James M. Robins. To appear in "Probabilistic and Causal Inference: The Works of Judea Pearl", Hector Geffner, Rina Dechter, and Joseph Y. Halpern, ACM Books, 2020 (forthcoming).
"An Interventionist Approach to Mediation Analysis," with Thomas S. Richardson, and James M. Robins. To appear in "Probabilistic and Causal Inference: The Works of Judea Pearl", Hector Geffner, Rina Dechter, and Joseph Y. Halpern, ACM Books, 2020 (forthcoming).
I. Shpitser. "Identification In Graphical Causal Models." In: Handbook of Graphical Models. Mathuus, Lauritzen and Wainwright, editors. Chapman & Hall, 2018.
Selected Recent Comments:
"Reflections on evolving conceptions of selection bias." with Maya Mathur. To appear in the American Journal of Epidemiology.
When does the ID algorithm fail?
Comment On: “Decision-Theoretic Foundations For Statistical Causality.” An invited commendary on Phillip Dawid's paper in the Journal of Causal Inference.
Comment On: “Assumption-lean inference for generalized linear model parameters." An invited commentary on Stijn Vansteelandt's and Oliver Dukes' read paper in Journal of the Royal Statistical Society, series B.
"Causal Modelling: The Two Cultures." with Elizabeth L. Ogburn. An invited commentary on Leo Breiman's "Statistical Modeling: The Two Cultures" in Observational Studies, Volume 7, Issue 1.
"Comment on "Blessings of Multiple Causes." with Elizabeth L. Ogburn and Eric Tchetgen Tchetgen.
"Comment: Causal Mediation of Semicompeting Risks." with Isabel Fulcher, Vanessa Didelez, Daniel Scharfstein, and Kali Zhou.
Selected Recent Papers:
"A common-cause principle for eliminating selection bias in causal estimands through covariate adjustment," with Maya Mathur and Tyler Vanderweele. To appear in the Annals of Statistics.
"Causal Inference with Hidden Mediators" with Amir Ghassami, Alan Yang, and Eric Tchetgen Tchetgen. To appear in Biometrika.
"Identification and Estimation for Nonignorable Missing Data: A Data Fusion Approach" with Zixiao Wang and Amir Ghassami. To appear in Proceedings of the 2024 Interventional Conference on Machine Learning (ICML-24).
"A General Identification Algorithm For Data Fusion Problems Under Systematic Selection" with Jaron Lee and Amir Ghassami. To appear in Proceedings of the 40th Conference on Uncertainty in Artificial Intelligence (UAI-24).
"Zero Inflation as a Missing Data Problem: a Proxy-based Approach" with Trung Phung, Jaron Lee, Opeyemi Oladapo-Shittu, Eili Klein, Ayse Pinar Gurses, Susa Hannum, Kimberly Weems, Jill Marsteller, Sara Cosgrove, and Sara Keller. To appear in Proceedings of the 40th Conference on Uncertainty in Artificial Intelligence (UAI-24).
"Simple graphical rules for assessing selection bias in general-population and selected-sample treatment effects" with Maya Mathur. To appear in the American Journal of Epidemiology.
"A Self-censoring Model for Multivariate Nonignorable Nonmonotone Missing Data" with Yilin Li, Wang Miao, and Eric Tchetgen Tchetgen. To appear in Biometrics.
"An Automated Approach to Causal Inference in Discrete Settings" with Guilherme Duarte, Noam Finkelstein, Dean Knox and Jonathan Mummolo. To appear in the Journal of the American Statistical Association.
"Examining the causal mediating role of cardiovascular disease on the effect of subclinical cardiovascular disease on cognitive impairment via separable effects" with Ryan Andrews, Vanessa Didelez, Paulo Chaves, Oscar Lopez, and Michelle Carlson. To appear in the Journal of Gerontology: Medical Sciences.
"Proximal Mediation Analysis." with Oliver Dukes and Eric J. Tchetgen Tchetgen. To appear in Biometrika.
"The Proximal ID Algorithm." with Zach Wood-Doughty and Eric J. Tchetgen Tchetgen. To appear in the Journal of Machine Learning Research (JMLR).
"The Lauritzen-Chen Likelihood For Graphical Models.". To appear in AISTATS 2023.
"Nested Markov Properties for Acyclic Directed Mixed Graphs." with Thomas S. Richardson, Robin Evans, and James M. Robins. To appear in the Annals of Statistics.
"Causal Discovery in Linear Latent Variable Models Subject to Measurement Error." with Yuqin Yang, AmirEmad Ghassami, Mohamed S Nafea, Negar Kiyavash, and Kun Zhang, To appear in Proceedings of NeurIPS 2022.
"Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables." with Rohit Bhattacharya and Razieh Nabi. To appear in the Journal of Machine Learning Research (JMLR).
"Semi-Parametric Causal Sufficient Dimension Reduction Of High Dimensional Treatments." with Razieh Nabi. In Proceedings of the Thirty Eighth Conference on Uncertainty in Artificial Intelligence (UAI-22), AUAI Press, 2022.
"An Interventionist Approach to Mediation Analysis," with Thomas S. Richardson, and James M. Robins. To appear in the Journal of Machine Learning Research (JMLR).
"Optimal Training of Fair Predictive Models." with Razieh Nabi and Dan Malinsky. To appear in CLEAR 2022.
"Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference." with Amir Ghassami, Andrew Ying, and Eric Tchetgen Tchetgen. To appear in AISTATS 2022.
"Causal Determinants of Postoperative Length of Stay in Cardiac Surgery Using Causal Graphical Learning," with J. J. R. Lee, R. Srinivasan, C. S. Ong, D. Alejo, S. Schena, M. Sussman, G. J. R. Whitman, and D. Malinsky. Forthcoming in Journal of Thoracic and Cardiovascular Surgery, 2022"Path-Dependent Structural Equation Models." with Ranjani Srinivasan, Jaron Lee, Rohit Bhattacharya. In Proceedings of the Thirty Seventh Conference on Uncertainty in Artificial Intelligence (UAI-21), AUAI Press, 2021.
"Entropic Inequality Constraints from e-separation Relations in Directed Acyclic Graphs with Hidden Variables." with Noam Finkelstein, Beata Zjawin, Elie Wolfe, and Rob Spekkens. In Proceedings of the Thirty Seventh Conference on Uncertainty in Artificial Intelligence (UAI-21), AUAI Press, 2021.
"Partial Identifiability in Discrete Data With Measurement Error." with Noam Finkelstein, Roy Adams, and Suchi Saria. In Proceedings of the Thirty Seventh Conference on Uncertainty in Artificial Intelligence (UAI-21), AUAI Press, 2021.
"Differentiable Causal Discovery Under Unmeasured Confounding." with Rohit Bhattacharya, Tushar Nagarajan, and Daniel Malinsky. To appear in AISTATS-21.
"Semiparametric Inference for Non-monotone Missing-Not-at-Random Data: the No Self-Censoring Model." with Daniel Malinsky and Eric Tchetgen Tchetgen. In the Journal of the American Statistical Association.
"Auto-g-computation of Causal Effects on a Network." with Eric J. Tchetgen Tchetgen and Isabel Fulcher. In the Journal of the American Statistical Association.
"Causal inference, social networks, and chain graphs." with Elizabeth Ogburn and Youjin Lee. In the Journal of the Royal Statistical Society, Series A.
"Full Law Identification In Graphical Models Of Missing Data: Completeness Results." with Razieh Nabi and Rohit Bhattacharya. In Proceedings of the Thirty-Seventh International Conference on Machine Learning, 2020.
"Identification And Estimation Of Causal Effects Defined By Shift Interventions." with Numair Sani and Jaron Lee. In Proceedings of the Thirty Sixth Conference on Uncertainty in Artificial Intelligence (UAI-20), AUAI Press, 2020.
"Deriving Bounds And Inequality Constraints Using Logical Relations Among Counterfactuals." with Noam Finkelstein. In Proceedings of the Thirty Sixth Conference on Uncertainty in Artificial Intelligence (UAI-20), AUAI Press, 2020.
"Examining the causal mediating role of brain pathology on the relationship between diabetes and cognitive impairment: The Cardiovascular Health Study." with Ryan M. Andrews, Oscar Lopez, William T. Longstreth, Paulo H. M. Chaves, Lew Kuller, and Michelle C. Carlson. In the Journal of the Royal Statistical Society, Series A.
Eli Sherman, David Arbour, I. Shpitser. "General Identification of Dynamic Treatment Regimes Under Interference." AISTATS 2020.
Isabel Fulcher, I. Shpitser, Stella Marealle and Eric J. Tchetgen Tchetgen. "Robust Inference On Population Indirect Causal Effects: The Generalized Front-Door Criterion." In the Journal of the Royal Statistical Society, Series B.
I. Shpitser. "Identification in Causal Models With Hidden Variables." In the Journal of the French Statistical Society. Société Française de Statistique, 2019.
C. H. Miles, I. Shpitser, P. Kanki, S. Meloni, and E. J. Tchetgen Tchetgen. "On semiparametric estimation of a path-specific effect in the presence of mediator-outcome confounding." In Biometrika. Oxford University Press, 2019.
Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser and James M. Robins. Identification In Missing Data Models Represented By Directed Acyclic Graphs." In Proceedings of the Thirty Fifth Conference on Uncertainty in Artificial Intelligence (UAI-19), AUAI Press, 2019.
Rohit Bhattacharya, Daniel Malinsky and Ilya Shpitser. "Causal Inference Under Interference and Network Uncertainty." In Proceedings of the Thirty Fifth Conference on Uncertainty in Artificial Intelligence (UAI-19), AUAI Press, 2019.
Eli Sherman and Ilya Shpitser. "Intervening on Network Ties." In Proceedings of the Thirty Fifth Conference on Uncertainty in Artificial Intelligence (UAI-19), AUAI Press, 2019.
Razieh Nabi, Daniel Malinsky and Ilya Shpitser. "Learning Optimal Fair Policies." In Proceedings of the Thirty-Sixth International Conference on Machine Learning (ICML-19).
Daniel Malinsky, Ilya Shpitser, and Thomas Richardson. "A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects." In Proceedings of the Twenty Second International Conference on Artificial Intelligence and Statistics (AISTATS-19).
Eli Sherman and Ilya Shpitser. "Identification and Estimation Of Causal Effects from Dependent Data." In Proceedings of the Third Second Annual Conference on Neural Information Processing Systems (NeurIPS-18), Curran Associates, Inc., 2018.
Zach Wood-Doughty, Ilya Shpitser, and Mark Drezde. "Challenges of Using Text Classifiers for Causal Inference." In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP-18).
Alex Gain and Ilya Shpitser. "Structure Learning Under Missing Data." In Proceedings of the 9th International Conference on Probabilistic Graphical Models (PGM-2018).
Ilya Shpitser, Robin J. Evans, and Thomas S. Richardson. "Acyclic Linear SEMs Obey the Nested Markov Property." In Proceedings of the Thirty Fourth Conference on Uncertainty in Artificial Intelligence (UAI-18), AUAI Press, 2018.
Razieh Nabi, Phyllis Kanki, and Ilya Shpitser. "Estimation of Personalized Effects Associated With Causal Pathways." In Proceedings of the Thirty Fourth Conference on Uncertainty in Artificial Intelligence (UAI-18), AUAI Press, 2018.
Ilya Shpitser and Eli Sherman. "Identification of Personalized Effects Associated With Causal Pathways." In Proceedings of the Thirty Fourth Conference on Uncertainty in Artificial Intelligence (UAI-18), AUAI Press, 2018.
Razieh Nabi and Ilya Shpitser. "Fair Inference on Outcomes." In Proceedings of the Thirty Second AAAI Conference on Artificial Intelligence (AAAI-18). AAAI Press, 2018.
Selected Recent Drafts:
"Ananke: A Python Package For Causal Inference Using Graphical Models," with J.J.R. Lee, R. Bhattacharya, and R. Nabi.
"Causal and counterfactual views of missing data models," with R. Nabi, R. Bhattacharya, and J. Robins.
"Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects," with A.E. Ghassami, A. Yang, D. Richardson, and E. Tchetgen Tchetgen.
"Modeling Interference Via Symmetric Treatment Decomposition." with Eric J. Tchetgen Tchetgen and Ryan M. Andrews.
Service (Past and Present):
- NIH study section member, ASP B.
- Program Chair: Thirty Ninth Conference on Uncertainty in Artificial Intelligence, 31st July - 4th August, Pittsburgh, PA, USA.
- Senior Program Committee (Area Chair): International Conference on Machine Learning (ICML), Neural Information Processing Systems Conference (NeurIPS), International Conference on Learning Representations (ICLR), International Joint Conference on Artificial Intelligence (IJCAI).
- Program Committee: Uncertainty in Artificial Intelligence (UAI), AAAI Conference on Artificial Intelligence (AAAI), European Conference on Artificial Intelligence (ECAI), International Conference on Artificial Intelligence and Statistics (AIStats)
- Reviewer for: Journal of Machine Learning Research, Artificial Intelligence, Annals of Statistics, Biometrika, Biometrics, Epidemiology, American Journal of Epidemiology, Journal of the Royal Statistical Society, International Journal of Biostatistics, Erkenntnis.
- Associate editor: Journal of Causal Inference.
- Associate editor: Journal of the American Statistical Association (Theory and Methods).
- Associate editor: American Journal of Epidemiology.
- Action editor: Journal of Machine Learning Research.
Funding:
- My research is supported in part by NSF , NIH , DARPA , and the Office of Naval Research.
Collaborators:
I am indebted to my collaborators and mentors: Judea Pearl, James M. Robins, Thomas S. Richardson, Eric J. Tchetgen Tchetgen, Tyler Vanderweele, Daniel Scharfstein, Robin J. Evans, Elizabeth Ogburn, Narges Ahmidi, Casey Overby-Taylor, and many others!