  Current supervised learning can learn spurious correlation during the
data-fitting process, imposing issues regarding interpretability,
out-of-distribution (OOD) generalization, and robustness. To avoid spurious
correlation, we propose a Latent Causal Invariance Model (LaCIM) which pursues
causal prediction. Specifically, we introduce latent variables that are
separated into (a) output-causative factors and (b) others that are spuriously
correlated to the output via confounders, to model the underlying causal
factors. We further assume the generating mechanisms from latent space to
observed data to be causally invariant. We give the identifiable claim of such
invariance, particularly the disentanglement of output-causative factors from
others, as a theoretical guarantee for precise inference and avoiding spurious
correlation. We propose a Variational-Bayesian-based method for estimation and
to optimize over the latent space for prediction. The utility of our approach
is verified by improved interpretability, prediction power on various OOD
scenarios (including healthcare) and robustness on security.
