==================================================================================================================================================
Cause-effect is a growing database with two-variable cause-effect pairs 
created at Max-Planck-Institute for Biological Cybernetics in Tuebingen, Germany.
==================================================================================================================================================

Some pairs are highdimensional, for machine readability the relevant information about this is coded in Meta-data.

Meta-data contains the following information:

number of pair | 1st column of cause | last column of cause | 1st column of effect | last column of effect | dataset weight

The dataset weight should be used for calculating average performance of causal inference methods
to avoid a bias introduced by having multiple copies of essentially the same data (for example,
the pairs 56-63).

NOTE: pair0001 - pair0041 are taken from the UCI Machine Learning Repository:

Asuncion, A. & Newman, D.J. (2007). UCI Machine Learning Repository [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, School of Information and Computer Science. 

==================================================================================================================================================
Overview over all data pairs.

			var 1				var 2					dataset			ground truth

pair0001		Altitude			Temperature				DWD			->
pair0002		Altitude			Precipitation				DWD			->
pair0003		Longitude			Temperature				DWD			->
pair0004		Altitude			Sunshine hours				DWD			->

pair0005		Age				Length					Abalone			->
pair0006		Age				Shell weight				Abalone			->
pair0007		Age				Diameter				Abalone			->
pair0008		Age				Height					Abalone			->
pair0009		Age				Whole weight				Abalone			->
pair0010		Age				Shucked weight				Abalone			->
pair0011		Age				Viscera weight				Abalone			->

pair0012		Age				Wage per hour				census income		->

pair0013		Displacement			Fuel consumption			auto-mpg		->
pair0014		Horse power			Fuel consumption			auto-mpg		->
pair0015		Weight				Fuel consumption			auto-mpg		->
pair0016		Horsepower			Acceleration				auto-mpg		->

pair0017		Age				Dividends from stocks			census income		->

pair0018		Age				Concentration GAG			GAGurine (from R package MASS)	->

pair0019		Current duration		Next interval				geyser			->

pair0020		Latitude			Temperature				DWD			->
pair0021		Longitude			Precipitation				DWD			->

pair0022		Age				Height					arrhythmia		->
pair0023		Age				Weight					arrhythmia		->
pair0024		Age				Heart rate				arrhythmia		->

pair0025		Cement				Compressive strength			concrete_data		->
pair0026		Blast furnace slag		Compressive strength			concrete_data		->
pair0027		Fly ash				Compressive strength			concrete_data		->
pair0028		Water				Compressive strength			concrete_data		->
pair0029		Superplasticizer		Compressive strength			concrete_data		->
pair0030		Coarse aggregate		Compressive strength			concrete_data		->
pair0031		Fine aggregate			Compressive strength			concrete_data		->
pair0032		Age				Compressive strength			concrete_data		->

pair0033		Alcohol consumption		Mean corpuscular volume 		liver disorders		->
pair0034		Alcohol consumption		Alkaline phosphotase 			liver disorders		->
pair0035		Alcohol consumption		Alanine aminotransferase		liver disorders		->
pair0036		Alcohol consumption		Aspartate aminotransferase		liver disorders		->
pair0037		Alcohol consumption		Gamma-glutamyl transpeptdase	 	liver disorders		->

pair0038		Age				Body mass index				pima indian diabetes	->
pair0039		Age				Serum insulin				pima indian diabetes	->
pair0040		Age				Diastolic blood pressure		pima indian diabetes	->
pair0041		Age				Plasma glucose concentration		pima indian diabetes	->

pair0042		Day of the year			Temperature				B.Janzing		->

pair0043		Temperature at t		Temperature at t+1			ncep-ncar		->		
pair0044		Pressure at t			Pressure at t+1				ncep-ncar		->		
pair0045		Sea level pressure at t		Sea level pressure at t+1		ncep-ncar		->		
pair0046		Relative humidity at t		Relative humidity at t+1		ncep-ncar		->		

pair0047		Number of cars			Type of day				traffic			<-

pair0048		Indoor temperature		Outdoor temperature			Hipel & Mcleod		<-

pair0049		Ozone concentration		Temperature				Bafu			<-
pair0050		Ozone concentration		Temperature				Bafu			<-
pair0051		Ozone concentration		Temperature				Bafu			<-

pair0052		(Temp, Press, SLP, Rh)		(Temp, Press, Slp, Rh)			ncep-ncar		<-

pair0053		Ozone concentration		(Wind speed, Radiation, Temperature)	environmental		<-

pair0054		(Displacement, Horsepower, Weight)  (Fuel consumption, Acceleration)	auto-mpg		->

pair0055		Ozone concentration (16-dim.)	Radiation (16-dim.)			Bafu			<-

pair0056		Female life expectancy, 2000-2005	Latitude			UNdata          	<-
pair0057		Female life expectancy, 1995-2000	Latitude			UNdata          	<-
pair0058		Female life expectancy, 1990-1995	Latitude			UNdata          	<-
pair0059		Female life expectancy, 1985-1990	Latitude			UNdata          	<-
pair0060		Male life expectancy, 2000-2005		Latitude			UNdata          	<-
pair0061		Male life expectancy, 1995-2000		Latitude			UNdata          	<-
pair0062		Male life expectancy, 1990-1995		Latitude			UNdata          	<-
pair0063		Male life expectancy, 1985-1990		Latitude			UNdata          	<-

pair0064		Drinking water access		Infant mortality			UNdata			->

pair0065		Stock return of Hang Seng Bank	Stock return of HSBC Hldgs		Yahoo database		->
pair0066		Stock return of Hutchison	Stock return of Cheung kong		Yahoo database		->
pair0067		Stock return of Cheung kong 	Stock return of Sun Hung Kai Prop.	Yahoo database		->

pair0068		Bytes sent 			Open http connections 			P. Stark & Janzing	<-

pair0069		Inside temperature		Outside temperature			J.M. Mooij		<-

pair0070		Parameter			Answer					Armann & Buelthoff	->

pair0071		Symptoms (6-dim.)		Classification of disease (2-dim.)	Acute Inflammations 	->

pair0072		Sunspots			Global mean temperature			sunspot data		->

pair0073		CO2 emissions			Energy use				UNdata			<-
pair0074		GNI per capita			Life expectancy				UNdata			->
pair0075		Under-5 mortality rate		GNI per capita				UNdata			<-
pair0076                Population growth		Food consumption growth			Food and Agriculture Organization of the United Nations    ->     

pair0077		Temperature			Solar radiation				B. Janzing       	<-

pair0078		PPFD				Net Ecosystem Productivity		Moffat A.M.		->
pair0079		Net Ecosystem Productivity	Diffuse PPFDdif				Moffat A.M.		<-
pair0080		Net Ecosystem Productivity	Direct PPFDdir				Moffat A.M.		<-

pair0081		Temperature 			Local CO2 flux, BE-Bra			Mahecha, M.		->
pair0082		Temperature 			Local CO2 flux, DE-Har			Mahecha, M.		->
pair0083		Temperature 			Local CO2 flux, US-PFa			Mahecha, M.		->

pair0084		Employment			Population				http://www.spatial-econometrics.com		<-

pair0085		Time of measurement		Protein content of milk			http://www.maths.lancs.ac.uk/Software/Oswald/	->

pair0086		Size of apartment		Monthly rent				J.M. Mooij					->

pair0087		Temperature			Total snow				http://www.mldata.org/repository/data/viewslug/whistler-daily-snowfall/		->

pair0088		Age				Relative spinal bone mineral density	"bone" dataset of R ElemStatLearn package	->

pair0089		root decomposition Oct (grassl)	root decomposition Oct (grassl)		Solly et al (2014). Plant and Soil, 382(1-2), 203-218.	<-
pair0090		root decomposition Oct (forest)	root decomposition Oct (forest)		Solly et al (2014). Plant and Soil, 382(1-2), 203-218.	<-
pair0091		clay cont. in soil (forest)	soil moisture				Solly et al (2014). Plant and Soil, 382(1-2), 203-218.	->
pair0092		organic carbon in soil (forest)	clay cont. in soil (forest)		Solly et al (2014). Plant and Soil, 382(1-2), 203-218.	<-

pair0093		precipitation			runoff					MOPEX (ftp://hydrology.nws.noaa.gov/pub/gcip/mopex/US_Data/Us_438_Daily/)

pair0094		hour of day			temperature				S. Armagan Tarim	->
pair0095		hour of day			electricity load			S. Armagan Tarim	->
pair0096		temperature			electricity load			S. Armagan Tarim	->

pair0097                speed at the beginning          speed at the end                        D. Janzing              ->     
pair0098                speed at the beginning          speed at the end                        D. Janzing              ->    

pair0099		language test score		social-economic status family		"nlschools" dataset of R MASS package		<-

pair0100		cycle time of CPU		performance				"cpus" dataset of R MASS package		->

pair0101                grey value of a pixel           brightness of the screen                D. Janzing                                      ->      

pair0102                position of a ball              time for passing a track segment        D. Janzing                                      ->

pair0103                position of a ball              time for passing a track segment        D. Janzing                                      ->
 
pair0104                time for passing 1. segment     time for passing 2. segment             D. Janzing                                      -> 

pair0105                pixel vector of a patch         total brightness at the screen          D. Janzing                                      -> 
    
pair0106                time required for one round     voltage                                 D. Janzing                                      <-   
 
