  Conventional supervised learning methods typically assume i.i.d samples and
are found to be sensitive to out-of-distribution (OOD) data. We propose
Generative Causal Representation Learning (GCRL) which leverages causality to
facilitate knowledge transfer under distribution shifts. While we evaluate the
effectiveness of our proposed method in human trajectory prediction models,
GCRL can be applied to other domains as well. First, we propose a novel causal
model that explains the generative factors in motion forecasting datasets using
features that are common across all environments and with features that are
specific to each environment. Selection variables are used to determine which
parts of the model can be directly transferred to a new environment without
fine-tuning. Second, we propose an end-to-end variational learning paradigm to
learn the causal mechanisms that generate observations from features. GCRL is
supported by strong theoretical results that imply identifiability of the
causal model under certain assumptions. Experimental results on synthetic and
real-world motion forecasting datasets show the robustness and effectiveness of
our proposed method for knowledge transfer under zero-shot and low-shot
settings by substantially outperforming the prior motion forecasting models on
out-of-distribution prediction. Our code is available at
https://github.com/sshirahmad/GCRL.
