---
license: bsd-3-clause
dataset_info:
  features:
  - name: cond_exp_y
    dtype: float64
  - name: m1
    dtype: float64
  - name: g1
    dtype: float64
  - name: l1
    dtype: float64
  - name: Y
    dtype: float64
  - name: D_1
    dtype: float64
  - name: carat
    dtype: float64
  - name: depth
    dtype: float64
  - name: table
    dtype: float64
  - name: price
    dtype: float64
  - name: review
    dtype: string
  - name: sentiment
    dtype: string
  - name: label
    dtype: int64
  - name: cut_Good
    dtype: bool
  - name: cut_Ideal
    dtype: bool
  - name: cut_Premium
    dtype: bool
  - name: cut_Very Good
    dtype: bool
  - name: color_E
    dtype: bool
  - name: color_F
    dtype: bool
  - name: color_G
    dtype: bool
  - name: color_H
    dtype: bool
  - name: color_I
    dtype: bool
  - name: color_J
    dtype: bool
  - name: clarity_IF
    dtype: bool
  - name: clarity_SI1
    dtype: bool
  - name: clarity_SI2
    dtype: bool
  - name: clarity_VS1
    dtype: bool
  - name: clarity_VS2
    dtype: bool
  - name: clarity_VVS1
    dtype: bool
  - name: clarity_VVS2
    dtype: bool
  - name: image
    dtype: image
  splits:
  - name: train
    num_bytes: 184009908.0
    num_examples: 50000
  download_size: 173099846
  dataset_size: 184009908.0
tags:
- Causal Inference
size_categories:
- 10K<n<100K
---

# Dataset Card

Semi-synthetic dataset with multimodal confounding.
The dataset is generated according to the description in [DoubleMLDeep: Estimation of Causal Effects with Multimodal Data](https://arxiv.org/abs/2402.01785).

## Dataset Details

### Dataset Description & Usage

The dataset contains the following columns:

### Dataset Sources

The dataset is based on the three commonly used datasets:

 - [Diamonds dataset](https://www.kaggle.com/datasets/shivam2503/diamonds)
 - [IMDB dataset](https://huggingface.co/datasets/imdb)
 - [CIFAR-10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html)

All datasets are subsampled to be of equal size (n=50,000). The CIFAR-10 data is based on the trainings dataset, whereas the IMDB data contains train and test data
to obtain 50,000 observations. The labels of the CIFAR-10 data are set to integer values 0 to 9.
The Diamonds dataset is cleaned (values with `x`, `y`, `z` equal to 0 are removed) and outliers are dropped (such that 45<`depth`<75, 40<`table`<80, `x`<30, `y`<30 and 2<`z`<30).
The remaining 53,907 observations are downsampled to the same size of 50,000 observations. Further `price` and `carat` are transformed with the natural logarithm and `cut`,
`color` and `clarity` are dummy coded (with baselines Fair, D and I1).

The versions to create this dataset can be found on Kaggle:

 - [Diamonds dataset](https://www.kaggle.com/datasets/shivam2503/diamonds)
 - [IMDB dataset](https://www.kaggle.com/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews?select=IMDB+Dataset.csv)
 - [CIFAR-10 dataset](https://www.kaggle.com/datasets/swaroopkml/cifar10-pngs-in-folders)

The original citations can be found below.

## Uses

The dataset should as a benchmark to compare different causal inference methods for observational data under multimodal confounding.

## Dataset Structure

### Data Instances

### Data Fields

The data fields can be devided into several categories:

- **Outcome and Treatments**
  - `Y` (`float64`): Outcome of interest
  - `D_1` (`float64`): Treatment value

- **Tabular Features**
  - `price` (`float64`): 

- **Text Features**
  - `review` (`string`): IMDB review text
  - `sentiment` (`string`): Corresponding 

- **Image Features**
  - `image` (`image`): Image
  - `label` (`int64`): Corresponding label from `0` to `9`

- **Oracle Features**
  - `cond_exp_y` (`float64`): Expected value `Y` conditional on `D_1`, etc. 
  - `D_1` (`float64`): Treatment value (generated)

## Limitations

As the confounding is generated via original labels, completely removing the confounding might not be possible.

## Citation Information

### Dataset Citation

If you use the dataset please cite this article:

```
@article{klaassen2024doublemldeep,
  title={DoubleMLDeep: Estimation of Causal Effects with Multimodal Data},
  author={Klaassen, Sven and Teichert-Kluge, Jan and Bach, Philipp and Chernozhukov, Victor and Spindler, Martin and Vijaykumar, Suhas},
  journal={arXiv preprint arXiv:2402.01785},
  year={2024}
}
```

### Dataset Sources

The three original datasets can be cited via

Diamonds dataset:

```
@Book{ggplot2_book,
  author = {Hadley Wickham},
  title = {ggplot2: Elegant Graphics for Data Analysis},
  publisher = {Springer-Verlag New York},
  year = {2016},
  isbn = {978-3-319-24277-4},
  url = {https://ggplot2.tidyverse.org},
}
```

IMDB dataset:

```
@InProceedings{maas-EtAl:2011:ACL-HLT2011,
  author    = {Maas, Andrew L.  and  Daly, Raymond E.  and  Pham, Peter T.  and  Huang, Dan  and  Ng, Andrew Y.  and  Potts, Christopher},
  title     = {Learning Word Vectors for Sentiment Analysis},
  booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
  month     = {June},
  year      = {2011},
  address   = {Portland, Oregon, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {142--150},
  url       = {http://www.aclweb.org/anthology/P11-1015}
}
```

CIFAR-10 dataset:

```
@TECHREPORT{Krizhevsky09learningmultiple,
    author = {Alex Krizhevsky},
    title = {Learning multiple layers of features from tiny images},
    institution = {},
    year = {2009}
}
```

## Dataset Card Authors

Sven Klaassen