  This paper is a write-up for the tutorial on "Fine-grained Interpretation and
Causation Analysis in Deep NLP Models" that we are presenting at NAACL 2021. We
present and discuss the research work on interpreting fine-grained components
of a model from two perspectives, i) fine-grained interpretation, ii) causation
analysis. The former introduces methods to analyze individual neurons and a
group of neurons with respect to a language property or a task. The latter
studies the role of neurons and input features in explaining decisions made by
the model. We also discuss application of neuron analysis such as network
manipulation and domain adaptation. Moreover, we present two toolkits namely
NeuroX and Captum, that support functionalities discussed in this tutorial.
