  Unsupervised meta-learning aims to learn the meta knowledge from unlabeled
data and rapidly adapt to novel tasks. However, existing approaches may be
misled by the context-bias (e.g. background) from the training data. In this
paper, we abstract the unsupervised meta-learning problem into a Structural
Causal Model (SCM) and point out that such bias arises due to hidden
confounders. To eliminate the confounders, we define the priors are
\textit{conditionally} independent, learn the relationships between priors and
intervene on them with casual factorization. Furthermore, we propose Causal
Meta VAE (CMVAE) that encodes the priors into latent codes in the causal space
and learns their relationships simultaneously to achieve the downstream
few-shot image classification task. Results on toy datasets and three benchmark
datasets demonstrate that our method can remove the context-bias and it
outperforms other state-of-the-art unsupervised meta-learning algorithms
because of bias-removal. Code is available at
\url{https://github.com/GuodongQi/CMVAE}
