Estimating effects with latent representations

A system comprising:

• a memory component;
• and
• a processing device coupled to the memory component, the processing device to perform operations comprising:
• receiving, via a network, input data describing interactions of client devices included in a group of client devices;
• generating a first latent vector representation of a first segment of the client devices and a second latent vector representation of a second segment of the client devices using an encoder of a machine learning model;
• computing a change vector based on a difference between the first latent vector representation and the second latent vector representation in a latent space of the machine learning model;
• and
• generating an indication of an effect of a treatment on a third segment of the client devices based on the change vector using a decoder of the machine learning model.

A non-transitory computer-readable storage medium storing executable instructions, which when executed by a processing device, cause the processing device to perform operations comprising:

• receiving, via a network, input data describing interactions of client devices included in a group of client devices, the input data includes categorical data and numerical data;
• representing the categorical data and the numerical data as a concatenated vector by batch normalizing the categorical data;
• generating a first latent vector representation of a first segment of the client devices and a second latent vector representation of a second segment of the client devices based on the concatenated vector using an encoder of a machine learning model;
• and
• generating an indication of an effect of a treatment on a third segment of the client devices based on a difference between the first latent vector representation and the second latent vector representation in a latent space using a decoder of the machine learning model.