DOI: 10.5281/zenodo.7886287
title: Cause-effect pairs from MPI Tübingen Collection

description: This collection of cause-effect pair datasets was created by the Max-Planck-Institute for Biological Cybernetics in Tuebingen, Germany (https://webdav.tuebingen.mpg.de/cause-effect/).

 

Task: Identify for each pair the causal direction using the observed samples only.

 

Summary: 



Size of collection: 83 datasets with 2 columns each of various sizes

Task: Causal Discovery

Data Type: Mixed

Dataset Scope: Collection

Ground Truth: Known

Temporal Structure: Static

License: TBD

Missing Values: No


 

Missingness Statement: There are no missing values.

 

Collection: The database contains the following datasets.






Number of dataset
Variable 1
Variable 2
Origin of datasets




001
Altitude
Temperature
DWD dataset


002
Altitude
Precipitation
DWD dataset


003
Longitude
Temperature
DWD dataset


004
Altitude
Sunshine hours
DWD dataset


005
Age
Length
Abalone dataset


006
Age
Shell weight
Abalone dataset


007
Age
Diameter
Abalone dataset


008
Age
Height
Abalone dataset


009
Age
Whole weight
Abalone dataset


010
Age
Shucked weight
Abalone dataset


011
Age
Viscera weight
Abalone dataset


012
Age
Wage per hour
Census income dataset


013
Displacement
Fuel consumption
Auto mpg dataset


014
Horse power
Fuel consumption
Auto mpg dataset


015
Weight
Fuel consumption
Auto mpg dataset


016
Horsepower
Acceleration
Auto mpg dataset


017
Age
Dividends from stocks
Census income dataset


018
Age
Concentration GA
R packages MASS


019
Current duration
Next interval
geyser


020
Latitude
Temperature
DWD dataset


021
Longitude
Precipitation
DWD


022
Age
Height
arrhythmia


023
Age
Weight
arrhythmia


024
Age
Heart rate
arrhythmia


025
Cement
Compressive strength
concrete_data


026
Blast furnace slag
Compressive strength concrete_data
 


027
Fly ash
Compressive strength
concrete_data


028
Water
Compressive strength
concrete_data


029
Superplasticizer
Compressive strength
concrete_data


030
Coarse aggregate
Compressive strength
concrete_data


031
Fine aggregate
Compressive strength
concrete_data


032
Age
 
Compressive strength


033
Alcohol consumption
Mean corpuscular volume
liver disorders


034
Alcohol consumption
Alkaline phosphotase
liver disorders


035
Alcohol consumption
Alanine aminotransferase
liver disorders


036
Alcohol consumption
Aspartate aminotransferase
liver disorders


037
Alcohol consumption
Gamma-glutamyl transpeptdase
liver disorders


038
Age
Body mass index
pima indian diabetes


039
Age
Serum insulin
pima indian diabetes


040
Age
Diastolic blood pressure
pima indian diabetes


041
Age
Plasma glucose concentration
pima indian diabetes


042
Day of the year
Temperature
B.Janzing


049
Ozone concentration
Temperature
Bafu


050
Ozone concentration
Temperature
Bafu


051
Ozone concentration
Temperature
Bafu


056
Female life expectancy, 2000-2005
Latitude
UNdata


057
Female life expectancy, 1995-2000
Latitude
UNdata


058
Female life expectancy, 1990-1995
Latitude
UNdata


059
Female life expectancy, 1985-1990
Latitude
UNdata


060
Male life expectancy, 2000-2005
Latitude
UNdata


061
Male life expectancy, 1995-2000
Latitude
UNdata


062
Male life expectancy, 1990-1995
Latitude
UNdata


068
Bytes sent
Open http connections
P. Stark & Janzing


072
Sunspots
Global mean temperature
sunspot data


076
Population growth
Food consumption growth
Food and Agriculture Organization of the United Nations


078
PPFD
Net Ecosystem Productivity
Moffat A.M.


079
Net Ecosystem Productivity
Diffuse PPFDdif
Moffat A.M.


080
Net Ecosystem Productivity Direct PPFDdir
Moffat A.M.
 


086
Size of apartment
Monthly rent
J.M. Mooij


088
Age
Relative spinal bone mineral density
“bone” dataset of R ElemStatLearn package


089
Mass loss Oct 2012 in %
Mass loss APRIL 2012 in %
Solly et al (2014). Plant and Soil, 382(1-2), 203-218


090
root decomposition Oct
(forest) root decomposition Apr
Solly et al (2014). Plant and Soil, 382(1-2), 203-218.


091
clay cont. in soil
(forest) soil moisture
Solly et al (2014). Plant and Soil, 382(1-2), 203-218.


092
organic carbon in soil (forest)
clay cont. in soil (forest)
Solly et al (2014). Plant and Soil, 382(1-2), 203-218.


093
precipitation
runoff
MOPEX (ftp://hydrology.nws.noaa.gov/pub/gcip/mopex/US_Data/Us_438_Daily/)


094
hour of day
temperature
S. Armagan Tarim


095
hour of day
electricity load
S. Armagan Tarim


096
temperature
electricity load
S. Armagan Tarim


097
speed at the beginning
speed at the end
The data has been recorded by Dominik Janzing using a ball track that has been equipped with two pairs of light barriers. The first pair measures the initial speed and the second pair the speed of a ball at some later position of the track. The units of the speeds are arbitrary and differ for both measurements (X and Y) since they are obtained by inverting the time the ball needed to pass the distance between two light barriers of one pair.


098
speed at the beginning
speed at the end
D. Janzing


099
language test score
social-economic status family
“nlschools” dataset of R MASS package


100
cycle time of CPU
performance
“cpus” dataset of R MASS package


101
grey value of a pixel
brightness of the screen
D. Janzing


102
position of a ball
time for passing a track segment
D. Janzing


103
position of a ball
time for passing a track segment
D. Janzing


106
time required for one round
voltage
D. Janzing


107
strength of contrast
answer correct or not
The data set is from a psychophysics experiments with human subjects. A screen shows tilted Gabor patches (which are patterns of stripes frequently used as stimuli in psychological experiments), either tilted to the left or to the right. The subject are asked to infer the direction, while the patches are shown with stronger or weaker contrast. The variable X describes the contrast values ranging from 0.0150 to 0.0500 in steps of 0.0025. The variable Y is a binary indicating whether the direction has been identified correctly (Y=1) or not (Y=0). For low values of the contrast the fraction of correct decisions approaches chance level (50%).


108
time for 1/6 rotation
temperature
This pair shows the dependence of the inverse velocity and the temperature of the heat bath of a Striling engine. The engine is driven by a cup of hot water that is put underneath.The inverse velocity is measured by the time needed for the engine’s wheel for 1/6 rotation (because the wheel has 6 radius arms). The temperature is measured by a sensor that was put into the cup. Recorded by D. Janzing







 

Files


More information about the dataset is contained in causal_description.html file.