Alexandre Drouin

Staff Research Scientist



Welcome to my page!

I’m a Staff Research Scientist at ServiceNow Research in Montreal and an Adjunct Professor of Computer Science at Laval University and Mila. I lead ServiceNow’s Human Decision Support research program, which explores the use of machine learning for decision-making in complex dynamic environments.

My main research interest is causal decision-making under uncertainty, where the goal is to answer questions of causal nature (interventions, counterfactual), while accounting for sources of uncertainty, such as ambiguity in causal structures and unmeasured variables. I am also interested in probabilistic time series forecasting and its use to foresee the long-term effect of actions.

In the past, I developed machine learning algorithms for biomarker discovery in large genomic datasets and focussed on applications to the global problem of antibiotic resistance. I obtained my Ph.D. in 2019 under the supervision of François Laviolette.


  • Machine learning
  • Causal inference and discovery
  • Time series forecasting
  • Model robustness


  • PhD in Artificial Intelligence, 2019

    Laval University

  • MSc in Artificial Intelligence, 2014

    Laval University

  • BSc in Computer Science, 2012

    Laval University


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(2023). Causal Discovery with Language Models as Imperfect Experts. ICML 2023 Workshop on Structured Probabilistic Inference & Generative Modeling.

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(2023). Invariant Causal Set Covering Machines. ICML 2023 Workshop on Spurious Correlations, Invariance, and Stability.

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(2023). Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts. 40th International Conference on Machine Learning (ICML 2023).

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(2022). TACTiS: Transformer-Attentional Copulas for Time Series. 39th International Conference on Machine Learning (ICML 2022) – Spotlight Presentation.

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(2022). Typing Assumptions Improve Identification in Causal Discovery. 1st Conference on Causal Learning and Reasoning (CLeaR 2022) – Oral Presentation.

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Multivariate probabilistic time series forecasting: transformer-based copulas and the limitations of proper scoring rules

Accurate estimation of time-varying quantities is crucial for effective decision-making across various domains, including healthcare …

Apprentissage automatique et raisonnement causal - Au delà des corrélations

La recherche en apprentissage automatique a rendu possibles d’époustouflantes avancées en intelligence artificielle. Un avenir où un …

Mind the structure: an introduction to causal inference for machine learning

Machine learning algorithms excel at discovering statistical dependency patterns in data. Those can be exploited to produce extremely …

Tutorial: A practical introduction to causal inference

This tutorial will consist of a practical introduction to the estimation of causal effects. We will experiment with the concepts of …

Differentiable Causal Discovery with Observational and Interventional Data

Knowledge of the causal structure that underlies a data generating process is essential to answering questions of causal nature. Such …

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