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A Benchmark for Bivariate Causal Discovery Methods

Käding, Christoph and Runge, Jakob (2021) A Benchmark for Bivariate Causal Discovery Methods. EGU General Assembly 2021, Online.

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Official URL: https://meetingorganizer.copernicus.org/EGU21/EGU21-8584.html


The Earth's climate is a highly complex and dynamical system. To better understand and robustly predict it, knowledge about its underlying dynamics and causal dependency structure is required. Since controlled experiments are infeasible in the climate system, observational data-driven approaches are needed. Observational causal inference is a very active research topic and a plethora of methods have been proposed. Each of these approaches comes with inherent strengths, weaknesses, and assumptions about the data generating process as well as further constraints. In this work, we focus on the fundamental case of bivariate causal discovery, i.e., given two data samples X and Y the task is to detect whether X causes Y or Y causes X. We present a large-scale benchmark that represents combinations of various characteristics of data-generating processes and sample sizes. By comparing most of the current state-of-the-art methods, we aim to shed light onto the real-world performance of evaluated methods. Since we employ synthetic data, we are able to precisely control the data characteristics and can unveil the behavior of methods when their underlying assumptions are met or violated. Further, we give a comparison on a set of real-world data with known causal relations to complete our evaluation.

Item URL in elib:https://elib.dlr.de/145149/
Document Type:Conference or Workshop Item (Speech, Poster)
Title:A Benchmark for Bivariate Causal Discovery Methods
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Date:April 2021
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
Keywords:Bivariate Causal Discovery, Benchmark
Event Title:EGU General Assembly 2021
Event Location:Online
Event Type:international Conference
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:other
DLR - Research area:Raumfahrt
DLR - Program:R - no assignment
DLR - Research theme (Project):R - no assignment
Location: Jena
Institutes and Institutions:Institute of Data Science > Datamangagement and Analysis
Deposited By: Käding, Christoph
Deposited On:08 Nov 2021 11:52
Last Modified:08 Nov 2021 11:52

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