5 citations · 8 across the 12 of their papers we have counts for
5 papers · 1 filter
The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications
Philippe Brouillard, Chandler Squires, Jonas Wahl +4
Causal discovery aims to automatically uncover causal relationships from data, a capability with significant potential across many scientific disciplines. However, its real-world a…
Causal Representation Learning in Temporal Data via Single-Parent Decoding
Philippe Brouillard, Sébastien Lachapelle, Julia Kaltenborn +6
Scientific research often seeks to understand the causal structure underlying high-level variables in a system. For example, climate scientists study how phenomena, such as El Niño…
Sparsity regularization via tree-structured environments for disentangled representations
Elliot Layne, Jason Hartford, Sébastien Lachapelle +2
Many causal systems such as biological processes in cells can only be observed indirectly via measurements, such as gene expression. Causal representation learning -- the task of c…
Demystifying amortized causal discovery with transformers
Francesco Montagna, Max Cairney-Leeming, Dhanya Sridhar +1
Supervised learning for causal discovery from observational data often achieves competitive performance despite seemingly avoiding the explicit assumptions that traditional methods…
Evaluating Interventional Reasoning Capabilities of Large Language Models
Tejas Kasetty, Divyat Mahajan, Gintare Karolina Dziugaite +2
Numerous decision-making tasks require estimating causal effects under interventions on different parts of a system. As practitioners consider using large language models (LLMs) to…