DeepAI AI Chat
Log In Sign Up

Differentiable Causal Discovery from Interventional Data

07/03/2020
by   Philippe Brouillard, et al.
6

Discovering causal relationships in data is a challenging task that involves solving a combinatorial problem for which the solution is not always 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.

READ FULL TEXT
09/06/2021

Learning Neural Causal Models with Active Interventions

Discovering causal structures from data is a challenging inference probl...
04/12/2023

DiscoGen: Learning to Discover Gene Regulatory Networks

Accurately inferring Gene Regulatory Networks (GRNs) is a critical and c...
04/16/2021

Shadow-Mapping for Unsupervised Neural Causal Discovery

An important goal across most scientific fields is the discovery of caus...
06/15/2022

Large-Scale Differentiable Causal Discovery of Factor Graphs

A common theme in causal inference is learning causal relationships betw...
09/14/2022

A Review and Roadmap of Deep Learning Causal Discovery in Different Variable Paradigms

Understanding causality helps to structure interventions to achieve spec...
12/10/2021

Learning soft interventions in complex equilibrium systems

Complex systems often contain feedback loops that can be described as cy...