![\"\"](\"https://storage.googleapis.com/kaggle-media/competitions/kaggle/3370/media/cause_effect_page_graphic.png\")
You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.
\r\n\r\n Privately sharing code or data outside of teams is not permitted.\r\n It's okay to share code if made available to all participants on the forums.\r\n
\r\nKaggle and the competition host have the right to publicly disseminate any entries or models.
\r\n\r\n Winning solutions need to be made available under\r\n a popular OSI-approved license\r\n in order to be eligible for recognition and prize money.\r\n
\r\n\r\n Prizes will be awarded after the winners have posted their solutions to the competition forum.\r\n Winners must post or link to their solutions within seven (7) days of being notified of their winning status.\r\n
\r\n\r\n Team mergers are allowed and can be performed by the team leader. In order to merge, the combined team must have a total submission count less than or equal to the maximum allowed as of the merge date. The maximum allowed is the number of submissions per day multiplied by the number of days the competition has been running.
\r\n
There is no maximum team size.
\r\nYou may submit a maximum of 5 entries per day.
\r\nYou may select up to 1 final submissions for judging.
\r\nStart Date: 3/29/2013 7:25 AM UTC
\r\nMerger Deadline: None
\r\nEntry Deadline: None
\r\nEnd Date: 9/2/2013 11:59 PM UTC
\r\n\t\r\nSee other rules. To be notified of any change, please register to our Google group.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Description", "content": "Come to our NIPS workshop (dec 9 or 10 in Tahoe).
\r\n\r\n
The problem of attributing causes to effects is pervasive in science, medicine, economy and almost every aspects of our everyday life involving human reasoning and decision making. What affects your health? the economy? climate changes? The gold standard to establish causal relationships is to perform randomized controlled experiments. However, experiments are costly while non-experimental \"observational\" data collected routinely around the world are readily available. Unraveling potential cause-effect relationships from such observational data could save a lot of time and effort.
\r\nConsider for instance a target variable B, like occurence of \"lung cancer\" in patients. The goal would be to find whether a factor A, like \"smoking\", might cause B. The objective of the challenge is to rank pairs of variables {A, B} to prioritize experimental verifications of the conjecture that A causes B.
\r\nAs is known, \"correlation does not mean causation\". More generally, observing a statistical dependency between A and B does not imply that A causes B or that B causes A; A and B could be consequences of a common cause. But, is it possible to determine from the joint observation of samples of two variables A and B that A should be a cause of B? There are new algorithms that have appeared in the literature in the past few years that tackle this problem. This challenge is an opportunity to evaluate them and propose new techniques to improve on them.
\r\nWe provide hundreds of pairs of real variables with known causal relationships from domains as diverse as chemistry, climatology, ecology, economy, engineering, epidemiology, genomics, medicine, physics. and sociology. Those are intermixed with controls (pairs of independent variables and pairs of variables that are dependent but not causally related) and semi-artificial cause-effect pairs (real variables mixed in various ways to produce a given outcome).
\r\nThis challenge is limited to pairs of variables deprived of their context. Thus constraint-based methods relying on conditional independence tests and/or graphical models are not applicable. The goal is to push the state-of-the art in complementary methods, which can eventually disambiguate Markov equivalence classes. If you are skeptical that this is possible, try this quiz: Examine the plot below of values of variable B plotted as a function of values of variable A. Can you guess which one is a cause of the other? Hint: Some non-linear functions are non-invertible.
\r\n![\"quiz](\"http://www.causality.inf.ethz.ch/images/ABexample.png\" "\\"A")
![\"Quiz](\"http://www.causality.inf.ethz.ch/images/answer.png\" "\\"Answer:")
July 1: A new data release was made to address a normalization problem and the deadline was extended. Scores on the public leaderboard prior to July 1 were decreased by 0.5. Please make new submissions with the new validation set. The competition is open to new teams.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Evaluation", "content": "For the purpose of this challenge, two variables A and B are causally related if:
\r\nB = f (A, noise) or A = f (B, noise).
\r\nIf the former case, A is cause of B and in the latter case B is a cause of A. All other factors are lumped into the \"noise\". We provide samples of joint observations of A and B, not organized in a time series. We exclude feed-back loops and consider only 4 types of relationships:
\r\n| A->B | \r\nA causes B | \r\nPositive class | \r\n
| B->A | \r\nB causes A | \r\nNegative class | \r\n
| A - B | \r\nA and B are consequences of a common cause | \r\nNull class | \r\n
| A | B | \r\nA and B are independent | \r\nNull class | \r\n
We bring the problem back to a classification problem: for each pair of variable {A, B}, you must answer the question: is A a cause of B? (or, since the problem is symmetrical in A and B, is B a cause of A?)
\r\nWe expect the participants to produce a score between -Inf and +Inf, large positive values indicating that A is a cause of B with certainty, large negative values indicating that B is a cause of A with certainty. Middle range scores (near zero) indicate that neither A causes B nor B causes A.
\r\nFor each pair of variables, we have a ternary truth value indicating whether A is a cause of B (+1), B is a cause of A (-1), or neither (0). We use the scores provided by the participants as a ranking criterion and evaluate their entries with the area under the ROC curve. See details.
\r\nWe make available Matlab and Python code to produce submissions from the data page.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Help Credits", "content": "This challenge is brought to you by CHALEARN, a non profit dedicated to the organization of machine learning challenges. We are grateful to KAGGLE for hosting the submission website.
\r\nIf you need help, see our Frequently Asked Questions or post a message on the Kaggle Forum.
\r\nThis project is sponsored by Pascal2 EU network of excellence and is part of the official selection of the IJCNN 2013 compettions. It is the results of the effort of many people, who are gratefully acknowledged.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "data-description", "content": "This is the July 1, 2013 final data release.
\r\nThe data provided on this page is in csv format, suitable to be read by:
\r\nArchived data and data in the split format (one pair per file) are also available.
\r\nThe file CEfinal_basic_python_benchmark.csv provides a sample submission.
\r\nWe released the final test data and an equivalent amount of training and validation data distributed similarly. The test data is encrypted, the decryption key will be revealed at the end of the development phase. The new validation set is replacing the old validation set on the leaderboard and all the scores are reset to 0.5, please re-submit results on the new validation set. The new data include pairs of variables generated in a similar way as those of SUP2data and pairs of real variables from various sources. The final data is different from the original training and validation data with respect to normalization and quantization of variables to address a problem of bias in the original data.
\r\nNEW: May-June 2013 supplementary data release:
\r\nWe provide three additional training datasets artificially generated: SUP1data, SUP2data, and SUP3data. Those training datasets have normalized numerical variables and have balanced number of unique values across all classes. SUP1data includes ~6000 pairs of numerical variables. SUP2 includes ~6000 pairs of mixed variables (numerical, categorical, binary). SUP3 data includes 81 pairs of real cause-effect pairs and 81 control pairs A|B and A-B generated from the real pairs.
\r\nMarch 2013 data release: archived.
\r\nIn CEfinal_xxx_text.zip, you will find the following files:
\r\nHere is the timeline for the competition:
\r\nCompetition prizes for the best ranking entrants, based on the final (test data) evaluation score on the Kaggle platform:
\r\n(*) The travel award may be used for one of the workshops organized in conjunction with the challenge (TBA). The award money will be granted in reimbursement of expenses including airfare, ground transportation, hotel, or workshop registration. Reimbursement is conditioned on (i) attenting the workshop, (ii) making an oral presentation of the methods used in the challenge, and (iii) presenting original receipts and boarding passes.
\r\nIn addition there will be four best paper awards, see details.
\r\nPrize eligibility is restricted by US export regulations and other challenge rules.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false } ] }