  Distributional representations of words, also known as word vectors, have
become crucial for modern natural language processing tasks due to their wide
applications. Recently, a growing body of word vector postprocessing algorithm
has emerged, aiming to render off-the-shelf word vectors even stronger. In line
with these investigations, we introduce a novel word vector postprocessing
scheme under a causal inference framework. Concretely, the postprocessing
pipeline is realized by Half-Sibling Regression (HSR), which allows us to
identify and remove confounding noise contained in word vectors. Compared to
previous work, our proposed method has the advantages of interpretability and
transparency due to its causal inference grounding. Evaluated on a battery of
standard lexical-level evaluation tasks and downstream sentiment analysis
tasks, our method reaches state-of-the-art performance.
