Audrey Poinsot

About Me

I am a third year Ph.D. student at INRIA (Paris, France) under the supervision of Marc Shoenauer and Alessandro Leite.

At the intersection of Causality and Machine Learning, my current research project aims to improve decision-support tools with a specific interest in robustness and uncertainty. Having worked as a consultant for a year before starting my Ph.D. funded by this same company, my research is driven by concrete use cases and applications.

My goal is to provide advise and knowledge in order to democratize the use of causal reasoning in statistical analysis for decision-making by providing tools to create value easily, sustainably, and fairly.

Research Interests

• Causality: causal data generation & augmentation, causal benchmarks, causal uncertainty quantification
• Machine Learning: Trustworthy ML (e.g. bias reduction, robustness, fairness, generalisation …)

News

• [Apr. 2025] Presenting to the “Causal Club” of University of Pisa
• [Feb. 2025] Presenting at the “AI, Sceince and Society Conference” part of the AI Action Summit Paris 2025
• [August 2024] Attending the 33rd International Joint Conference on Artificial Intelligence (IJCAI 2024)
• [June-Sept. 2024] Research visitor in the Causal Artificial Intelligence Lab of the Department of Computer Science of Columbia University, New York
• [Jan. 2024] Presenting at Young Statisticians and Probabilists Day 2024
• [Sept. 2023] Attending the school “Tools for Causality”
• [July 2023] Created a Hackathon, “Estimating Marketing Uplifts as Heterogeneous Treatment Effects with Meta-Learners”, for the “Study week Causal Inference for Industry” at University Paris Saclay, subject
• [June 2023] Presenting at the Quarter on Causality, “Causality in Practice”
• [May 2023] Attending the Quarter’s second week of conference “Fundamental Challenges in Causality”
• [May 2023] Attending the 11th International Conference on Learning Representations (ICLR 2023)
• [Apr. 2023] Chairing and attending the Quarter’s first week of conference “When Causal Inference meets Statistical Analysis”
• [Apr. 2023] Chairing and attending the opening session of our Thematic Quarter on Causality
• [Apr. 2023] Our Thematic Quarter on Causality strats with a week school on causality
• [Sept. 2022] Attending the 2022 ELLIS Doctoral Symposium in ALicante, Spain.
• [June 2022] Attending the Machine Learning Summer School (MLSS22) in Krakow, Poland

Publications

Talks

Presentations

• Learning Structural Causal Models through Deep Generative Models: Methods, Guarantees, and Challenges, at Causal Club, University of Pisa, slides
• Learning Structural Causal Models through Deep Generative Models: Methods, Guarantees, and Challenges, at 33rd International Joint Conference on Artificial Intelligence 2024, slides
• Causal Data Augmentation: Causality to serve Machine Learning, at Young Statisticians and Probabilists Day 2024, slides
• Reconciling Mix Marketing Modelling and Causal Inference, at Causality in Practice (Quarter on Causality) 2023, slides

Posters

• AI, Sceince and Society Conference part of the AI Action Summit Paris 2025
• 33rd International Joint Conference on Artificial Intelligence 2024, poster
• Workshop on Pitfalls of limited data and computation for Trustworthy ML (ICLR) 2023
• When Causal Inference meets Statistical Analysis (Quarter on Causality) 2023
• Opening session of the Quarter on Causality 2023
• ELLIS Doctoral Symposium 2022

Services

Conference Reviewers

• When Causal Inference meets Statistical Analysis (Quarter on Causality) 2023

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