  Drawing causal conclusions from observational real-world data is a very much
desired but challenging task. In this paper we present mixed-method analyses to
investigate causal influences of publication trends and behavior on the
adoption, persistence, and retirement of certain research foci --
methodologies, materials, and tasks that are of interest to the computational
linguistics (CL) community. Our key findings highlight evidence of the
transition to rapidly emerging methodologies in the research community (e.g.,
adoption of bidirectional LSTMs influencing the retirement of LSTMs), the
persistent engagement with trending tasks and techniques (e.g., deep learning,
embeddings, generative, and language models), the effect of scientist location
from outside the US, e.g., China on propensity of researching languages beyond
English, and the potential impact of funding for large-scale research programs.
We anticipate this work to provide useful insights about publication trends and
behavior and raise the awareness about the potential for causal inference in
the computational linguistics and a broader scientific community.
