Causal Discovery Toolbox: Uncover causal relationships in Python

3 code implementations • 6 Mar 2019 • Diviyan Kalainathan, Olivier Goudet

This paper presents a new open source Python framework for causal discovery from observational data and domain background knowledge, aimed at causal graph and causal mechanism modeling.

Causal Discovery

Structural Agnostic Modeling: Adversarial Learning of Causal Graphs

1 code implementation • 13 Mar 2018 • Diviyan Kalainathan, Olivier Goudet, Isabelle Guyon, David Lopez-Paz, Michèle Sebag

A new causal discovery method, Structural Agnostic Modeling (SAM), is presented in this paper.

Causal Discovery

Causal Generative Neural Networks

1 code implementation • ICLR 2018 • Olivier Goudet, Diviyan Kalainathan, Philippe Caillou, Isabelle Guyon, David Lopez-Paz, Michèle Sebag

We present Causal Generative Neural Networks (CGNNs) to learn functional causal models from observational data.

Causal Discovery

Learning Functional Causal Models with Generative Neural Networks

2 code implementations • 15 Sep 2017 • Olivier Goudet, Diviyan Kalainathan, Philippe Caillou, Isabelle Guyon, David Lopez-Paz, Michèle Sebag

We introduce a new approach to functional causal modeling from observational data, called Causal Generative Neural Networks (CGNN).