
![celeba2.png](..%2F..%2F..%2FDownloads%2Fceleba2.png)

We all have seen these types of Male to Female image translation by generative models which is quite impressive.
But have you ever wondered what facial features a model exactly introduce while
this translation? Does it introduce facial features such as "wearing lipstic" or "makeup" ?
What other features is it adding to the generated images? Is it adding some
features that it should not?

In our recent paper, we evaluated two image generative models that were pre-trained on the popular Celebrity faces dataset (CelebA) 
with a classifier and observed the following bar plot.
![img.png](img.png)
We observed that addition of some attributes are logical such as  "Wearing_Lipstic" (82%), "Heavy_Makeup"(69%), 
"Wearing_Earrings" (33%) or "No_Beard" (30%) which can be considered causally related sex (Male/Female).
Some attributes are added even though they are non-causal. For example: 37% of the translated images are detected having the 
"Attractive" attribute (an attribute of the CelebA dataset) and 24% of images are having the "Young" attribute.

We analysed the reason behind this as following:
The CelebA dataset contains more frequency of young female or attractive female images compared to other combinations.
The image translation models memorize the correlations between "Female" and "Young" or between "Female" and "Attractive" 
during their training. As a result, during translation they do not consider if an attribute is causally related to Sex or not,
they add it based on the spurious correlation they learned. Thus, we see more young female in the translated images although we do not
expect it.

In our paper, we proposed a sound and complete algorithm called IDGEN that can estimate causal effects involving high-dimensional
variables such as images. It can generate high-dimensional interventional samples given that we know the causal graph and 
the causal query is identifiable from observational data.
