D'ya like DAGs? A Survey on Structure Learning and Causal Discovery

03/03/2021
by   Matthew J. Vowels, et al.
4

Causal reasoning is a crucial part of science and human intelligence. In order to discover causal relationships from data, we need structure discovery methods. We provide a review of background theory and a survey of methods for structure discovery. We primarily focus on modern, continuous optimization methods, and provide reference to further resources such as benchmark datasets and software packages. Finally, we discuss the assumptive leap required to take us from structure to causality.

READ FULL TEXT

page 1

page 2

page 3

page 4

11/12/2016

A Review on Algorithms for Constraint-based Causal Discovery

Causal discovery studies the problem of mining causal relationships betw...
03/06/2019

Causal Discovery Toolbox: Uncover causal relationships in Python

This paper presents a new open source Python framework for causal discov...
04/04/2022

Causality, Causal Discovery, and Causal Inference in Structural Engineering

Much of our experiments are designed to uncover the cause(s) and effect(...
08/30/2021

A practical guide to causal discovery with cohort data

In this guide, we present how to perform constraint-based causal discove...
03/15/2012

Learning Why Things Change: The Difference-Based Causality Learner

In this paper, we present the Difference- Based Causality Learner (DBCL)...
04/28/2021

Causal Discovery of Flight Service Process Based on Event Sequence

The development of the civil aviation industry has continuously increase...
09/22/2020

Using Unsupervised Learning to Help Discover the Causal Graph

The software outlined in this paper, AitiaExplorer, is an exploratory ca...