Handling Data with Interventions

Hi,

Thank you for the excellent repository! It has been exciting exploring the discovery tools in this repository lately. I was using the data from Sachs et al. 2005 before I realized the data was already implemented as an internal dataset here. I am running into some confusion, however, and thought I would ask here.

For clarity's sake, here is how I am loading in the internal dataset:

from causallearn.utils.Dataset import load_dataset
data, labels = load_dataset(dataset_name="sachs")
df_internal = pd.DataFrame(data=data,columns=labels)

From what I can tell, the internal implementation of the dataset is some subset of the 14 excel tables one retrieves if they download from the paper directly. First, I noticed the internal dataset contains exactly the same columns as all 14 of the excel tables I have from the paper. The rows, by contrast, differ substantially. There are on the order of 11 thousand rows present across all 14 tables but there are only around 7 thousand rows present in the internal dataset. (I did also confirm that the first 5 rows of the internal dataset match exactly to those of the 1. cd3cd28.xls file, so it does not look like any normalization/processing has altered the values themselves).

Taken together, it seems the internal dataset is a row-joined subset of the original Sachs dataset. Is this a correct assessment? If so, what subset of the tables are included? Why aren't all conditions included?

Please let me know if I have simply missed some tutorial or documentation somewhere. Any assistance would be greatly appreciated.

Overall, my goal is to reproduce the graph seen in Figure 3A. I know the authors used a simulated annealing approach, but I want to try more current approaches.

Start at the load_dataset entry point and inspect how the internal "sachs" dataset relates to the 14 Sachs et al. 2005 Excel tables. Compare the included rows and conditions with the source tables. Done means documenting which subset is included, why other conditions are absent, and whether it supports reproducing Figure 3A.