Johan de Aguas

• Pérez, Johan Manuel de Aguas & Jullum, Martin (2026). Scope-Restricted Backtracking Counterfactuals. Proceedings of Machine Learning Research (PMLR). 343. doi: https:/openreview.net/forum?id=BEb4fBP6LI. Fulltekst i vitenarkiv
• Pérez, Johan Manuel de Aguas (2026). Collaborative Representations for Targeted Causal Inference Under Outcome Selection. I Ceci, Michelangelo; Kocev, Dragi; Monreale, Anna; Vens, Celine; Flach, Peter; Manco, Giuseppe; Spiliopoulou, Myra & Masciari, Elio (Red.), Machine Learning and Knowledge Discovery in Databases. Research Track. Springer Nature Switzerland. ISSN 9783032376541. s. 623–640. doi: 10.1007/978-3-032-37654-1_36. Fulltekst i vitenarkiv
• Pérez, Johan Manuel de Aguas (2026). Interpolated Stochastic Interventions Based on Propensity Scores, Target Policies and Treatment-Specific Costs. Proceedings of the AAAI Conference on Artificial Intelligence. ISSN 2159-5399. 40(43), s. 36654–36662. doi: 10.1609/aaai.v40i43.40989. Fulltekst i vitenarkiv
• Pérez, Johan Manuel de Aguas; Henckel, Leonard; Pensar, Johan & Biele, Guido (2025). Causal Inference amid Missingness-Specific Independencies and Mechanism Shifts. Proceedings of Machine Learning Research (PMLR). 286, s. 31–42. doi: https:/proceedings.mlr.press/v286/aguas25a.html. Fulltekst i vitenarkiv Vis sammendrag

  The recovery of causal effects in structural models with missing data often relies on m-graphs, which assume that missingness mechanisms do not directly influence substantive variables. Yet, in many real-world settings, missing data can alter decision-making processes, as the absence of key information may affect downstream actions and states. To overcome this limitation, we introduce lm-SCMs and lm-graphs, which extend m-graphs by integrating a label set that represents relevant context-specific independencies (CSI), accounting for mechanism shifts induced by missingness. We define two causal effects within these systems: the Full Average Treatment Effect (FATE), which reflects the effect in a hypothetical scenario had no data been missing, and the Natural Average Treatment Effect (NATE), which captures the effect under the unaltered CSIs in the system. We propose recovery criteria for these queries and present doubly-robust estimators for a graphical model inspired by a real-world application. Simulations highlight key differences between these estimands and estimation methods. Findings from the application case suggest a small effect of ADHD treatment upon test achievement among Norwegian children, with a slight effect shift due to missing pre-tests scores.

• Pérez, Johan Manuel de Aguas; Henckel, Leonard; Pensar, Johan & Biele, Guido (2025). Causal Inference amid Missingness-Specific Independences and Mechanism Shifts. Proceedings of Machine Learning Research (PMLR). 286, s. 31–44. Fulltekst i vitenarkiv
• Pérez, Johan Manuel de Aguas; Krumscheid, Sebastian; Pensar, Johan & Biele, Guido (2025). The Probability of Tiered Benefit: Partial Identification with Robust and Stable Inference. Proceedings of Machine Learning Research (PMLR). 275, s. 90–113. doi: https:/proceedings.mlr.press/v275/aguas25a.html. Fulltekst i vitenarkiv
• Pérez, Johan Manuel de Aguas; Pensar, Johan; Pérez, Tomás Fernando Varnet & Biele, Guido (2025). Recovery and inference of causal effects with sequential adjustment for confounding and attrition. Journal of Causal Inference. ISSN 2193-3677. 13(1). doi: 10.1515/jci-2024-0009. Fulltekst i vitenarkiv
• Biele, Guido; Pérez, Johan Manuel de Aguas & Pérez, Tomás Fernando Varnet (2023). What can we conclude about the effect of parental income on offspring mental health? International Journal of Epidemiology. ISSN 0300-5771. 52(2), s. 641–643. doi: 10.1093/ije/dyac144. Fulltekst i vitenarkiv

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• Abdulkadir, Mohammed A.; Alay, Özgü; Dreibholz, Thomas; Elmokashfi, Ahmed; Li, Xi & Lopez, Diego [Vis alle 13 forfattere av denne artikkelen] (2021). AI and ML – Enablers for Beyond 5G Networks. 5G PPP Technology Board. doi: 10.5281/zenodo.429989. Fulltekst i vitenarkiv Vis sammendrag

  This white paper on AI and ML as enablers of beyond 5G (B5G) networks is based on contributions from almost 20 5G PPP projects, coordinated through the 5G PPP Technology Board, that research, implement and validate 5G and B5G network systems. The paper introduces the main relevant mechanisms in Artificial Intelligence (AI) and Machine Learning (ML), currently investigated and exploited for enhancing 5G and B5G networks.

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• Melteig, Elina; Glad, Ingrid Kristine; Ramezani-Kebrya, Ali; Pérez, Johan Manuel de Aguas; Holmsen, Sigurd & Palomares, Alfonso Diz-Lois [Vis alle 7 forfattere av denne artikkelen] (2026). Blir KI vår dommedag? Ekspertene strides. [Journal]. Titan. Fulltekst i vitenarkiv
• Pérez, Johan Manuel de Aguas; Pensar, Johan; Pérez, Tomás Fernando Varnet & Biele, Guido (2024). Recovering target causal effects from post-exposure selection induced by missing outcome data. Fulltekst i vitenarkiv
• Pérez, Johan Manuel de Aguas; Pensar, Johan & Biele, Guido (2024). Partial identification and efficient estimation for the stratum-specific probability of benefit with thresholds on a continuous outcome. Fulltekst i vitenarkiv
• Pérez, Johan Manuel de Aguas; Pensar, Johan; Pérez, Tomás Fernando Varnet & Biele, Guido (2024). Recovering target causal effects from post-exposure selection induced by missing outcome data. Fulltekst i vitenarkiv
• Pérez, Johan Manuel de Aguas; Pensar, Johan & Biele, Guido (2023). Recovering causal effects from confounding and selection biases: Extended graphical criteria via mediation analysis. Fulltekst i vitenarkiv
• Pérez, Johan Manuel de Aguas; Pensar, Johan & Biele, Guido (2023). Recovering causal effects from confounding and selection biases: Extended graphical criteria via mediation analysis. Fulltekst i vitenarkiv
• Pérez, Johan Manuel de Aguas; Pensar, Johan & Biele, Guido (2023). Counterfactual inference with nonparametric Bayes: Causal attribution of ADHD treatment on level achievement at the national test. Fulltekst i vitenarkiv

Se alle arbeider i NVA