Portrait de Philippe Brouillard

Philippe Brouillard

philippebrouillard@gmail.com
Doctorat - UdeM
Superviseur⋅e principal⋅e
Dhanya Sridhar
Co-supervisor
Alexandre Drouin
Sujets de recherche
Causalité
Site web
Google Scholar
GitHub

Publications

A Meta-Learning Approach to Causal Inference
Dragos Cristian Manta
Philippe Brouillard
Dhanya Sridhar
Predicting the effect of unseen interventions is at the heart of many scientific endeavours. While causal discovery is often used to answer … (voir plus)these causal questions, it involves learning a full causal model, not tailored to the specific goal of predicting unseen interventions, and operates under stringent assumptions. We introduce a novel method based on meta-learning that predicts interventional effects without explicitly assuming a causal model. Our preliminary results on synthetic data show that it can provide good generalization to unseen interventions, and it even compares favorably to a causal discovery method. Our model-agnostic method opens up many avenues for future exploration, particularly for settings where causal discovery cannot be applied.
2025-06-09
ICML.cc/2025/Workshop/SIM (poster)
openreview.net
openreview.net
The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications
Philippe Brouillard
Chandler Squires
Jonas Wahl
Konrad Paul Kording
Karen Sachs
Alexandre Drouin
Dhanya Sridhar
Causal discovery aims to automatically uncover causal relationships from data, a capability with significant potential across many scientifi… (voir plus)c disciplines. However, its real-world applications remain limited. Current methods often rely on unrealistic assumptions and are evaluated only on simple synthetic toy datasets, often with inadequate evaluation metrics. In this paper, we substantiate these claims by performing a systematic review of the recent causal discovery literature. We present applications in biology, neuroscience, and Earth sciences - fields where causal discovery holds promise for addressing key challenges. We highlight available simulated and real-world datasets from these domains and discuss common assumption violations that have spurred the development of new methods. Our goal is to encourage the community to adopt better evaluation practices by utilizing realistic datasets and more adequate metrics.
2025-01-28
cclear.cc/CLeaR/2025/Conference (poster)
doi.org
openreview.net
The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications
Philippe Brouillard
Chandler Squires
Jonas Wahl
Konrad P. Kording
Karen Sachs
Alexandre Drouin
Dhanya Sridhar
Causal discovery aims to automatically uncover causal relationships from data, a capability with significant potential across many scientifi… (voir plus)c disciplines. However, its real-world applications remain limited. Current methods often rely on unrealistic assumptions and are evaluated only on simple synthetic toy datasets, often with inadequate evaluation metrics. In this paper, we substantiate these claims by performing a systematic review of the recent causal discovery literature. We present applications in biology, neuroscience, and Earth sciences - fields where causal discovery holds promise for addressing key challenges. We highlight available simulated and real-world datasets from these domains and discuss common assumption violations that have spurred the development of new methods. Our goal is to encourage the community to adopt better evaluation practices by utilizing realistic datasets and more adequate metrics.
2024-12-02
ArXiv (prépublication)
doi.org
arxiv.org
The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications
Philippe Brouillard
Chandler Squires
Jonas Wahl
Konrad P. Kording
Karen Sachs
Alexandre Drouin
Dhanya Sridhar
Causal discovery aims to automatically uncover causal relationships from data, a capability with significant potential across many scientifi… (voir plus)c disciplines. However, its real-world applications remain limited. Current methods often rely on unrealistic assumptions and are evaluated only on simple synthetic toy datasets, often with inadequate evaluation metrics. In this paper, we substantiate these claims by performing a systematic review of the recent causal discovery literature. We present applications in biology, neuroscience, and Earth sciences - fields where causal discovery holds promise for addressing key challenges. We highlight available simulated and real-world datasets from these domains and discuss common assumption violations that have spurred the development of new methods. Our goal is to encourage the community to adopt better evaluation practices by utilizing realistic datasets and more adequate metrics.
2024-12-02
ArXiv (prépublication)
arxiv.org
arxiv.org
Causal Representation Learning in Temporal Data via Single-Parent Decoding
Philippe Brouillard
Sébastien Lachapelle
Julia Kaltenborn
Yaniv Gurwicz
Dhanya Sridhar
Alexandre Drouin
Peer Nowack
Jakob Runge
David Rolnick
Scientific research often seeks to understand the causal structure underlying high-level variables in a system. For example, climate scienti… (voir plus)sts study how phenomena, such as El Ni\~no, affect other climate processes at remote locations across the globe. However, scientists typically collect low-level measurements, such as geographically distributed temperature readings. From these, one needs to learn both a mapping to causally-relevant latent variables, such as a high-level representation of the El Ni\~no phenomenon and other processes, as well as the causal model over them. The challenge is that this task, called causal representation learning, is highly underdetermined from observational data alone, requiring other constraints during learning to resolve the indeterminacies. In this work, we consider a temporal model with a sparsity assumption, namely single-parent decoding: each observed low-level variable is only affected by a single latent variable. Such an assumption is reasonable in many scientific applications that require finding groups of low-level variables, such as extracting regions from geographically gridded measurement data in climate research or capturing brain regions from neural activity data. We demonstrate the identifiability of the resulting model and propose a differentiable method, Causal Discovery with Single-parent Decoding (CDSD), that simultaneously learns the underlying latents and a causal graph over them. We assess the validity of our theoretical results using simulated data and showcase the practical validity of our method in an application to real-world data from the climate science field.
2024-10-09
ArXiv (prépublication)
doi.org
arxiv.org
Towards Causal Representations of Climate Model Data
Julien Boussard
Chandni Nagda
Julia Kaltenborn
Charlotte Emilie Elektra Lange
Philippe Brouillard
Yaniv Gurwicz
Peer Nowack
David Rolnick
Climate models, such as Earth system models (ESMs), are crucial for simulating future climate change based on projected Shared Socioeconomic… (voir plus) Pathways (SSP) greenhouse gas emissions scenarios. While ESMs are sophisticated and invaluable, machine learning-based emulators trained on existing simulation data can project additional climate scenarios much faster and are computationally efficient. However, they often lack generalizability and interpretability. This work delves into the potential of causal representation learning, specifically the \emph{Causal Discovery with Single-parent Decoding} (CDSD) method, which could render climate model emulation efficient \textit{and} interpretable. We evaluate CDSD on multiple climate datasets, focusing on emissions, temperature, and precipitation. Our findings shed light on the challenges, limitations, and promise of using CDSD as a stepping stone towards more interpretable and robust climate model emulation.
2023-12-05
ArXiv (prépublication)
doi.org
arxiv.org
ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning
Julia Kaltenborn
Charlotte Emilie Elektra Lange
Venkatesh Ramesh
Philippe Brouillard
Yaniv Gurwicz
Chandni Nagda
Jakob Runge
Peer Nowack
David Rolnick
Climate models have been key for assessing the impact of climate change and simulating future climate scenarios. The machine learning (ML) c… (voir plus)ommunity has taken an increased interest in supporting climate scientists’ efforts on various tasks such as climate model emulation, downscaling, and prediction tasks. Many of those tasks have been addressed on datasets created with single climate models. However, both the climate science and ML communities have suggested that to address those tasks at scale, we need large, consistent, and ML-ready climate model datasets. Here, we introduce ClimateSet, a dataset containing the inputs and outputs of 36 climate models from the Input4MIPs and CMIP6 archives. In addition, we provide a modular dataset pipeline for retrieving and preprocessing additional climate models and scenarios. We showcase the potential of our dataset by using it as a benchmark for ML-based climate model emulation. We gain new insights about the performance and generalization capabilities of the different ML models by analyzing their performance across different climate models. Furthermore, the dataset can be used to train an ML emulator on several climate models instead of just one. Such a “super-emulator” can quickly project new climate change scenarios, complementing existing scenarios already provided to policymakers. We believe ClimateSet will create the basis needed for the ML community to tackle climate-related tasks at scale.
openreview.net
Two types of human TCR differentially regulate reactivity to self and non-self antigens
Assya Trofimov
Philippe Brouillard
Jean-David Larouche
Jonathan Séguin
Jean-Philippe Laverdure
Ann Brasey
Grégory Ehx
Denis-Claude Roy
Lambert Busque
Silvy Lachance
Sébastien Lemieux
Claude Perreault
2022-08-17
iScience (publié)
doi.org
Two types of human TCR differentially regulate reactivity to self and non-self antigens
Assya Trofimov
Philippe Brouillard
Jean-David Larouche
Jonathan Y. Séguin
Jean-Philippe Laverdure
A. Brasey
Grégory Ehx
D. Roy
Lambert Busque
Silvy Lachance
Sébastien Lemieux
Claude Perreault
2022-04-28
bioRxiv (preprint)
doi.org
Differentiable Causal Discovery from Interventional Data
Philippe Brouillard
Sébastien Lachapelle
Alexandre Lacoste
Simon Lacoste-Julien
Alexandre Drouin
Discovering causal relationships in data is a challenging task that involves solving a combinatorial problem for which the solution is not a… (voir plus)lways identifiable. A new line of work reformulates the combinatorial problem as a continuous constrained optimization one, enabling the use of different powerful optimization techniques. However, methods based on this idea do not yet make use of interventional data, which can significantly alleviate identifiability issues. In this work, we propose a neural network-based method for this task that can leverage interventional data. We illustrate the flexibility of the continuous-constrained framework by taking advantage of expressive neural architectures such as normalizing flows. We show that our approach compares favorably to the state of the art in a variety of settings, including perfect and imperfect interventions for which the targeted nodes may even be unknown.
arxiv.org
G RADIENT -B ASED N EURAL DAG L EARNING WITH I NTERVENTIONS
Philippe Brouillard
Alexandre Drouin
Sébastien Lachapelle
Alexandre Lacoste
Simon Lacoste-Julien
Decision making based on statistical association alone can be a dangerous endeavor due to non-causal associations. Ideally, one would rely o… (voir plus)n causal relationships that enable reasoning about the effect of interventions. Several methods have been proposed to discover such relationships from observational and inter-ventional data. Among them, GraN-DAG, a method that relies on the constrained optimization of neural networks, was shown to produce state-of-the-art results among algorithms relying purely on observational data. However, it is limited to observational data and cannot make use of interventions. In this work, we extend GraN-DAG to support interventional data and show that this improves its ability to infer causal structures