add oracle features

README.md

@@ -101,12 +101,6 @@ The dataset is based on the three commonly used datasets:
  - [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)
@@ -115,6 +109,14 @@ The versions to create this dataset can be found on Kaggle:
 
 The original citations can be found below.
 
+### Dataset Preprocessing
+
+All datasets are subsampled to be of equal size (`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`).
+
 ## Uses
 
 The dataset should as a benchmark to compare different causal inference methods for observational data under multimodal confounding.
@@ -131,20 +133,22 @@ The data fields can be devided into several categories:
   - `Y` (`float64`): Outcome of interest
   - `D_1` (`float64`): Treatment value
 
-- **Tabular Features**
-  - `price` (`float64`): 
-
 - **Text Features**
   - `review` (`string`): IMDB review text
-  - `sentiment` (`string`): Corresponding 
+  - `sentiment` (`string`): Corresponding sentiment, either `positive` or `negative`
 
 - **Image Features**
   - `image` (`image`): Image
   - `label` (`int64`): Corresponding label from `0` to `9`
 
+- **Tabular Features**
+  - `price` (`float64`):
+
 - **Oracle Features**
-  - `cond_exp_y` (`float64`): Expected value `Y` conditional on `D_1`, etc. 
-  - `D_1` (`float64`): Treatment value (generated)
+  - `cond_exp_y` (`float64`): Expected value of `Y` conditional on `D_1`, `sentiment`, `label` and `price`
+  - `l1` (`float64`): Expected value of `Y` conditional on `sentiment`, `label` and `price`
+  - `m1` (`float64`): Expected value of `D_1` conditional on `sentiment`, `label` and `price`
+  - `g1` (`float64`): Additive component of `Y` based on `sentiment`, `label` and `price`
 
 ## Limitations