  We propose a causal interpretation of self-attention in the Transformer
neural network architecture. We interpret self-attention as a mechanism that
estimates a structural equation model for a given input sequence of symbols
(tokens). The structural equation model can be interpreted, in turn, as a
causal structure over the input symbols under the specific context of the input
sequence. Importantly, this interpretation remains valid in the presence of
latent confounders. Following this interpretation, we estimate conditional
independence relations between input symbols by calculating partial
correlations between their corresponding representations in the deepest
attention layer. This enables learning the causal structure over an input
sequence using existing constraint-based algorithms. In this sense, existing
pre-trained Transformers can be utilized for zero-shot causal-discovery. We
demonstrate this method by providing causal explanations for the outcomes of
Transformers in two tasks: sentiment classification (NLP) and recommendation.
