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HTTP/2 200 x-powered-by: PHP/8.3.23 content-type: text/html; charset=UTF-8 content-length: 4103 content-encoding: gzip vary: Accept-Encoding date: Mon, 29 Dec 2025 08:57:44 GMT server: LiteSpeed platform: hostinger panel: hpanel content-security-policy: upgrade-insecure-requests alt-svc: h3=":443"; ma=2592000, h3-29=":443"; ma=2592000, h3-Q050=":443"; ma=2592000, h3-Q046=":443"; ma=2592000, h3-Q043=":443"; ma=2592000, quic=":443"; ma=2592000; v="43,46" Juan D. Correa - Academic Webpage

Juan D. Correa
Assistant Professor
CS Department - Universidad Autónoma de Manizales
jcorrea [at] autonoma [dot] edu [dot] co
juandavidcorreagranada

I obtained my Ph.D. from the Computer Science Department at Columbia University, where I was adviced by Professor Elias Bareinboim.
My research is on the fundamental conditions and methods to make causal claims from data collected in heterogenous domains, obtained by experimentation or under sampling selection bias.

News

Nov, 2020 I presented my work on σ-calculus at the Causal Data Science Meeting, 2020. (Slides )
Oct, 2020 Our paper on transportability of soft interventions has been accepted to NeurIPS-20.

Publications

New General Transportability of Soft Interventions: Completeness Results

Juan D. Correa, Elias Bareinboim
Columbia Causal AI Lab, Technical Report (R-68), Jun 2020.
NeurIPS-20. In Proceedings of the 34th Annual Conference on Neural Information Processing Systems, , forthcoming.

On Pearl's Hierarchy and the Foundations of Causal Inference

Elias Bareinboim, Juan D. Correa, Duligur Ibeling, Thomas Icard
Columbia Causal AI Lab, Technical Report (R-60), Jul 2020.
ACM-20. In Probabilistic and Causal Inference: The Works of Judea Pearl (ACM Special Turing Series), forthcoming.

A Calculus for Stochastic Interventions: Causal Effect Identification and Surrogate Experiments

Juan D. Correa, Elias Bareinboim
Columbia Causal AI Lab, Technical Report (R-55), Nov 2019.
AAAI-20. In Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020.

Generalized Transportability: Synthesis of Experiments from Heterogeneous Domains

Sanghack Lee, Juan D. Correa, Elias Bareinboim
Columbia Causal AI Lab, Technical Report (R-53), Nov 2019.
AAAI-20. In Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020.

Identifiability from a Combination of Observations and Experiments

Sanghack Lee, Juan D. Correa, Elias Bareinboim
Columbia Causal AI Lab, Technical Report (R-52), Nov 2019.
AAAI-20. In Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020.

General Identifiability with Arbitrary Surrogate Experiments

Sanghack Lee, Juan D. Correa, Elias Bareinboim
Purdue AI Lab, Technical Report (R-46), May 2019.
UAI-19. In In Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence, 2019.
Best Paper Award (1 out of 450 papers).

From Statistical Transportability to Estimating the Effects of Stochastic Interventions

Juan D. Correa, Elias Bareinboim
Purdue AI Lab, Technical Report (R-45), May 2019.
IJCAI-19. In Proceedings of the 27th International Joint Conference on Artificial Intelligence, 2019.

Adjustment Criteria for Generalizing Experimental Findings

Juan D. Correa, Jin Tian, Elias Bareinboim
Purdue AI Lab, Technical Report (R-43), Apr 2019.
ICML-19. In Proceedings of the 36th International Conference on Machine Learning, 2019.

Identification of Causal Effects in the Presence of Selection Bias

Juan D. Correa, Jin Tian, Elias Bareinboim
Purdue AI Lab, Technical Report (R-38), Nov 2018.
AAAI-19. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence, 2019.

Generalized Adjustment Under Confounding and Selection Biases

Juan D. Correa, Jin Tian, Elias Bareinboim
Purdue AI Lab, Technical Report (R-29), Nov 2017.
AAAI-18. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence, 2018.
Outstanding Paper Award Honorable Mention (2 out of 3800 papers).

Causal Effect Identification by Adjustment under Confounding and Selection Biases

Juan D. Correa, Elias Bareinboim
Purdue AI Lab, Technical Report (R-24), Nov 2016.
AAAI-17. In Proceedings of the 31st AAAI Conference on Artificial Intelligence, 2017.

Pre-PhD

Grid Computing and CBR Deployment: Monitoring Principles for a Suitable Engagement

Luis F. Castillo, Gustavo Isaza, Manuel G. Bedia, Miguel Aguilera, Juan D. Correa
Distributed Computing and Artificial Intelligence, 2012.

Teaching

Graduate Teaching Assistant - Columbia University
- COMS4995W Causal Inference (Fall 2020)
- COMS4995W Causal Inference (Spring 2020)
Graduate Teaching Assistant - Purdue University
- CS590-AML Advanced Machine Learning (Spring 2019)
- CS471 Intro to Artificial Intelligence (Spring 2018)
- CS590-AI Artificial Intelligence (Fall 2017)
- CS471 Intro to Artificial Intelligence (Spring 2017)
- CS590-AI Artificial Intelligence (Fall 2016)
Lecturer - Universidad Autonoma de Manizales
- Introduction to Programming (2014-2015)
- Object Oriented Programming (2013-2014)
Lecturer - Universidad de Caldas
- Introduction to Programming (2013)
- Introduction to Artificial Intelligence (2013)

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