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Merge pull request #6 from Theavinash02/Tubingen_datasets
Tubingen datasets update in example_datasets
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README.md

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1. [example-causal-datasets](https://github.com/cmu-phil/example-causal-datasets): CC0 1.0 Universal. Last synced on
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2026-02-05.
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2. [Tuebingen-pair-wise-dataset](https://webdav.tuebingen.mpg.de/cause-effect/): Last downloaded on 2026-03-02.

pairwise-tubingen/README

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==================================================================================================================================================
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Cause-effect is a growing database with two-variable cause-effect pairs
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created at Max-Planck-Institute for Biological Cybernetics in Tuebingen, Germany.
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==================================================================================================================================================
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Some pairs are highdimensional, for machine readability the relevant information about this is coded in Meta-data.
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Meta-data contains the following information:
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number of pair | 1st column of cause | last column of cause | 1st column of effect | last column of effect | dataset weight
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The dataset weight should be used for calculating average performance of causal inference methods
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to avoid a bias introduced by having multiple copies of essentially the same data (for example,
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the pairs 56-63).
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When you use this data set in a publication, please cite the following paper (which
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also contains much more detailed information regarding this data set in the supplement):
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J. M. Mooij, J. Peters, D. Janzing, J. Zscheischler, B. Schoelkopf
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"Distinguishing cause from effect using observational data: methods and benchmarks"
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Journal of Machine Learning Research 17(32):1-102, 2016
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NOTE: pair0001 - pair0041 are taken from the UCI Machine Learning Repository:
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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.
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==================================================================================================================================================
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Overview over all data pairs.
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var 1 var 2 dataset ground truth
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pair0001 Altitude Temperature DWD ->
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pair0002 Altitude Precipitation DWD ->
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pair0003 Longitude Temperature DWD ->
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pair0004 Altitude Sunshine hours DWD ->
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pair0005 Age Length Abalone ->
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pair0006 Age Shell weight Abalone ->
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pair0007 Age Diameter Abalone ->
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pair0008 Age Height Abalone ->
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pair0009 Age Whole weight Abalone ->
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pair0010 Age Shucked weight Abalone ->
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pair0011 Age Viscera weight Abalone ->
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pair0012 Age Wage per hour census income ->
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pair0013 Displacement Fuel consumption auto-mpg ->
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pair0014 Horse power Fuel consumption auto-mpg ->
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pair0015 Weight Fuel consumption auto-mpg ->
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pair0016 Horsepower Acceleration auto-mpg ->
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pair0017 Age Dividends from stocks census income ->
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pair0018 Age Concentration GAG GAGurine (from R package MASS) ->
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pair0019 Current duration Next interval geyser ->
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pair0020 Latitude Temperature DWD ->
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pair0021 Longitude Precipitation DWD ->
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pair0022 Age Height arrhythmia ->
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pair0023 Age Weight arrhythmia ->
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pair0024 Age Heart rate arrhythmia ->
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pair0025 Cement Compressive strength concrete_data ->
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pair0026 Blast furnace slag Compressive strength concrete_data ->
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pair0027 Fly ash Compressive strength concrete_data ->
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pair0028 Water Compressive strength concrete_data ->
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pair0029 Superplasticizer Compressive strength concrete_data ->
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pair0030 Coarse aggregate Compressive strength concrete_data ->
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pair0031 Fine aggregate Compressive strength concrete_data ->
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pair0032 Age Compressive strength concrete_data ->
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pair0033 Alcohol consumption Mean corpuscular volume liver disorders ->
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pair0034 Alcohol consumption Alkaline phosphotase liver disorders ->
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pair0035 Alcohol consumption Alanine aminotransferase liver disorders ->
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pair0036 Alcohol consumption Aspartate aminotransferase liver disorders ->
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pair0037 Alcohol consumption Gamma-glutamyl transpeptdase liver disorders ->
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pair0038 Age Body mass index pima indian diabetes ->
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pair0039 Age Serum insulin pima indian diabetes ->
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pair0040 Age Diastolic blood pressure pima indian diabetes ->
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pair0041 Age Plasma glucose concentration pima indian diabetes ->
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pair0042 Day of the year Temperature B.Janzing ->
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pair0043 Temperature at t Temperature at t+1 ncep-ncar ->
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pair0044 Pressure at t Pressure at t+1 ncep-ncar ->
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pair0045 Sea level pressure at t Sea level pressure at t+1 ncep-ncar ->
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pair0046 Relative humidity at t Relative humidity at t+1 ncep-ncar ->
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pair0047 Number of cars Type of day traffic <-
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pair0048 Indoor temperature Outdoor temperature Hipel & Mcleod <-
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pair0049 Ozone concentration Temperature Bafu <-
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pair0050 Ozone concentration Temperature Bafu <-
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pair0051 Ozone concentration Temperature Bafu <-
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pair0052 (Temp, Press, SLP, Rh) (Temp, Press, Slp, Rh) ncep-ncar <-
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pair0053 Ozone concentration (Wind speed, Radiation, Temperature) environmental <-
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pair0054 (Displacement, Horsepower, Weight) (Fuel consumption, Acceleration) auto-mpg ->
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pair0055 Ozone concentration (16-dim.) Radiation (16-dim.) Bafu <-
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pair0056 Female life expectancy, 2000-2005 Latitude UNdata <-
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pair0057 Female life expectancy, 1995-2000 Latitude UNdata <-
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pair0058 Female life expectancy, 1990-1995 Latitude UNdata <-
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pair0059 Female life expectancy, 1985-1990 Latitude UNdata <-
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pair0060 Male life expectancy, 2000-2005 Latitude UNdata <-
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pair0061 Male life expectancy, 1995-2000 Latitude UNdata <-
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pair0062 Male life expectancy, 1990-1995 Latitude UNdata <-
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pair0063 Male life expectancy, 1985-1990 Latitude UNdata <-
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pair0064 Drinking water access Infant mortality UNdata ->
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pair0065 Stock return of Hang Seng Bank Stock return of HSBC Hldgs Yahoo database ->
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pair0066 Stock return of Hutchison Stock return of Cheung kong Yahoo database ->
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pair0067 Stock return of Cheung kong Stock return of Sun Hung Kai Prop. Yahoo database ->
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pair0068 Bytes sent Open http connections P. Stark & Janzing <-
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pair0069 Inside temperature Outside temperature J.M. Mooij <-
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pair0070 Parameter Answer Armann & Buelthoff ->
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pair0071 Symptoms (6-dim.) Classification of disease (2-dim.) Acute Inflammations ->
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pair0072 Sunspots Global mean temperature sunspot data ->
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pair0073 CO2 emissions Energy use UNdata <-
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pair0074 GNI per capita Life expectancy UNdata ->
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pair0075 Under-5 mortality rate GNI per capita UNdata <-
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pair0076 Population growth Food consumption growth Food and Agriculture Organization of the United Nations ->
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pair0077 Temperature Solar radiation B. Janzing <-
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pair0078 PPFD Net Ecosystem Productivity Moffat A.M. ->
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pair0079 Net Ecosystem Productivity Diffuse PPFDdif Moffat A.M. <-
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pair0080 Net Ecosystem Productivity Direct PPFDdir Moffat A.M. <-
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pair0081 Temperature Local CO2 flux, BE-Bra Mahecha, M. ->
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pair0082 Temperature Local CO2 flux, DE-Har Mahecha, M. ->
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pair0083 Temperature Local CO2 flux, US-PFa Mahecha, M. ->
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pair0084 Employment Population http://www.spatial-econometrics.com <-
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pair0085 Time of measurement Protein content of milk http://www.maths.lancs.ac.uk/Software/Oswald/ ->
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pair0086 Size of apartment Monthly rent J.M. Mooij ->
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pair0087 Temperature Total snow http://www.mldata.org/repository/data/viewslug/whistler-daily-snowfall/ ->
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pair0088 Age Relative spinal bone mineral density "bone" dataset of R ElemStatLearn package ->
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pair0089 root decomposition Oct (grassl) root decomposition Oct (grassl) Solly et al (2014). Plant and Soil, 382(1-2), 203-218. <-
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pair0090 root decomposition Oct (forest) root decomposition Oct (forest) Solly et al (2014). Plant and Soil, 382(1-2), 203-218. <-
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pair0091 clay cont. in soil (forest) soil moisture Solly et al (2014). Plant and Soil, 382(1-2), 203-218. ->
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pair0092 organic carbon in soil (forest) clay cont. in soil (forest) Solly et al (2014). Plant and Soil, 382(1-2), 203-218. <-
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pair0093 precipitation runoff MOPEX (ftp://hydrology.nws.noaa.gov/pub/gcip/mopex/US_Data/Us_438_Daily/)
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pair0094 hour of day temperature S. Armagan Tarim ->
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pair0095 hour of day electricity load S. Armagan Tarim ->
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pair0096 temperature electricity load S. Armagan Tarim ->
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pair0097 speed at the beginning speed at the end D. Janzing ->
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pair0098 speed at the beginning speed at the end D. Janzing ->
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pair0099 language test score social-economic status family "nlschools" dataset of R MASS package <-
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pair0100 cycle time of CPU performance "cpus" dataset of R MASS package ->
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pair0101 grey value of a pixel brightness of the screen D. Janzing ->
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pair0102 position of a ball time for passing a track segment D. Janzing ->
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pair0103 position of a ball time for passing a track segment D. Janzing ->
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pair0104 time for passing 1. segment time for passing 2. segment D. Janzing ->
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pair0105 pixel vector of a patch total brightness at the screen D. Janzing ->
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pair0106 time required for one round voltage D. Janzing <-
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pair0107 strength of contrast answer correct or not Schuett, edited by D. Janzing ->
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pair0108 time for 1/6 rotation temperature D. Janzing <-
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