Causality Causality Workbench                                                             Challenges in Machine Learning Causality
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SIDO: A phamacology dataset

Contact: Isabelle Guyon - Submitted: 2008-09-12 02:53 - Views : 1980

This is one of the datasets of the first causality challenge: causation and prediction. The goal of the challenge was to make predictions under manipulations. SIDO (SImple Drug Operation mechanisms) contains descriptors of molecules, which have...  [more/question/discuss/rate/edit...]

PROMO: Simple causal effects in time series

Contact: Jean-Philippe Pellet - Submitted: 2011-01-26 17:59 - Views : 3600

The PROMO dataset proposes the task to identify which promotions affect sales. Artificial data about 1000 promotion variables and 100 product sales is provided. The goal is to predict a 1000x100 boolean influence matrix, indicating for each (i,j)...  [more/question/discuss/rate/edit...]

TIED: Target Information Equivalent Dataset

Contact: Alexander Statnikov - Submitted: 2008-09-12 20:24 - Views : 2471

TIED dataset 2008 Alexander Statnikov and Constantin Aliferis Introduction TIED stands for Target Information Equivalent Dataset. It is an artificial simulated dataset constructed to illustrate that there may be many minimal sets of...  [more/question/discuss/rate/edit...]

CINA: A marketing dataset

Contact: Isabelle Guyon - Submitted: 2008-09-12 02:37 - Views : 2628

CINA (Census Is Not Adult) is derived from census data (the UCI machine-learning repository Adult database). The data consists of census records for a number of individuals. The causal discovery task is to uncover the socio-economic factors...  [more/question/discuss/rate/edit...]

  • Authors: Causality workbench team
  • Key facts: Number of variables: 132 (demographic data) + one binary target variable . Number of examples: training 16033 + 3 test sets of 10000 examples corresponding...
  • Keywords: probe.method, marketing

REGED: A genomics dataset

Contact: Isabelle Guyon - Submitted: 2008-09-12 02:55 - Views : 1824

This is one of the datasets of the first causality challenge: causation and prediction. The goal of the challenge was to make predictions under manipulations. REGED (REsimulated Gene Expression Dataset) monitors the expression of genes, which...  [more/question/discuss/rate/edit...]

  • Authors: Causality workbench team
  • Key facts: Number of variables: 999 (gene expression coefficients) + one binary target variable (health status). Number of examples: training 500 + 3 test sets of 20000...
  • Keywords: bayesian.network, genomics

MARTI: Measurement Artifacts

Contact: Isabelle Guyon - Submitted: 2008-09-12 06:02 - Views : 1711

This is one of the datasets of the first causality challenge: causation and prediction. The goal of the challenge was to make predictions under manipulations. MARTI (Measurement ARTIfact) is obtained from the same data generative process as...  [more/question/discuss/rate/edit...]