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Merge pull request #5 from stephanielees/feature/add_new_data_NSLM
add a new dataset from NSLM
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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.
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3. [Twins-datasets](http://www.nber.org/data/linked-birth-infant-death-data-vital-statistics-data.html)
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2. [nslm](https://github.com/grf-labs/grf/tree/master/experiments/acic18): CC0 1.0 Universal. Last downloaded on 2026-03-04.
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3. [Tuebingen-pair-wise-dataset](https://webdav.tuebingen.mpg.de/cause-effect/): Last downloaded on 2026-03-02.
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4. [Twins-datasets](http://www.nber.org/data/linked-birth-infant-death-data-vital-statistics-data.html)

nslm/LICENSE

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nslm/README.md

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The dataset is from the National Study of Learning Mindsets and is mentioned in the paper titled "Estimating Treatment Effects with Causal Forests: An Application" by Susan Athey and Stefan Wager.
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The National Study of Learning Mindsets was a study conducted in some of the US public high schools. This dataset emulates that observational study with similar characteristics. It is designed to explore whether a small nudge from teachers at school could affect students' behaviour.
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The variables are the following:
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- schoolid: The ID of the schools which students participate in the experiment.
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- Z: Whether a student/participant recieves intervention. 1 means yes, 0 means no.
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- Y: The participants' score, which are taken after the experiment.
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- S3: The participants' expectation of success. This is measured before the experiment is executed.
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- C1: Student race/ethnicity
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- C2: Student identified gender
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- C3: Student first-generation status to go to college
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- XC: School-level variable: the location of the school (i.e. rural, suburban, etc.)
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- X1: School-level mean of students' fixed mindsets, reported before the experiment.
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- X2: School test scores and college preparation for the previous four cohorts.
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- X3: School racial/ethnic minority distribution
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- X4: School poverty concentration
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- X5: Total students in all four grade levels in the high school.
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To learn further about the dataset: https://github.com/grf-labs/grf/tree/master/experiments/acic18
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Paper reference:
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Susan Athey, & Stefan Wager. (2019). Estimating Treatment Effects with Causal Forests: An Application.

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