Towards the Reusability and Compositionality of Causal Representations
Davide Talon Phillip Lippe Stuart James Alessio Del Bue Sara Magliacane
Causal Representation Learning (CRL) aims at identifying high-level causal
factors and their relationships from high-dimensional observations, e.g.,
images. While most CRL works focus on learning causal representations in a
single environment, in this work we instead propose a first step towards
learning causal representations from temporal sequences of images that can be
adapted in a new environment, or composed across multiple related environments.
In particular, we introduce DECAF, a framework that detects which causal
factors can be reused and which need to be adapted from previously learned
causal representations. Our approach is based on the availability of
intervention targets, that indicate which variables are perturbed at each time
step. Experiments on three benchmark datasets show that integrating our
framework with four state-of-the-art CRL approaches leads to accurate
representations in a new environment with only a few samples.