<jupyter_start><jupyter_text>READMESimple analysis notebooks for collected causality data. 为每个因果推断数据集合做一个简单的 data analysis.  awesome-causality-dataAn index of datasets that can be used for learning causality. Please cite our survey if this data index helps your research.@article{guo2018survey,  title={A Survey of Learning Causality with Data: Problems and Methods},  author={<NAME> and <NAME> and <NAME> and <NAME> and <NAME>},   journal={arXiv preprint arXiv:1809.09337},   year={2018}}*Updates coming soon*   Datasets for Learning Causal Effects (Causal Inference) With Back-door Criterion Standard Datasets for Causal InferenceStandard datasets for learning causal effects comes with each instance in the format of (**x**,d,y).[IHDP1](https://github.com/AMLab-Amsterdam/CEVAE/tree/master/datasets/IHDP)[How is IHDP1 (setting A) simulated](https://github.com/vdorie/npci/tree/master/examples/ihdp_sim)[IHDP2](https://math.la.asu.edu/~prhahn/)[Twins](https://github.com/AMLab-Amsterdam/CEVAE/tree/master/datasets/TWINS)[Job Training](http://users.nber.org/~rdehejia/data/nswdata2.html) (Lalonde 1986)[ACIC Benchmark](https://github.com/vdorie/aciccomp/tree/master/2016) Non-i.i.d. Datasets for Causal Inference[Amazon](https://drive.google.com/drive/u/1/folders/1Ff_GdfjhrDFbZiRW0z81lGJW-cUrYmo1) Without Back-door Criterion Datasets with instrumental Variables (IV)Standard datasets for learning causal effects, each instance has the format of (i,**x**,d,y).[1980 Census Extract](https://economics.mit.edu/faculty/angrist/data1/data/angkru95)[CPS Extract](https://economics.mit.edu/faculty/angrist/data1/data/angkru95) Datasets for Regression Discontinuity Design[Population Threshold RDD Datasets](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/PGXO5O)  Datasets for Learning Causal Relationships (Causal Discovery) Distinguishing Cause from Effect[Database with cause-effect pairs](http://webdav.tuebingen.mpg.de/cause-effect/)  Datasets for Connections to Machine Learning Datasets with randomized test set for recommendation systems|Name|Paper|URL||---|---|---||Coat|Schnabel, Tobias, <NAME>, <NAME>, <NAME>, and <NAME>. "Recommendations as treatments: Debiasing learning and evaluation." arXiv preprint arXiv:1602.05352 (2016).|[download](http://www.cs.cornell.edu/~schnabts/mnar/index.html)||Yahoo! R3|   |[download](https://webscope.sandbox.yahoo.com/catalog.php?datatype=r)|<jupyter_code>'Standard Datasets for Causal Inference'.replace(' ', '_')
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