is the information in Y explainable by both L(T ) and C, then our goal is to choose L such that this variance is maximized after C has been fixed.
with this in mind, we formalize the task of deconfounded lexicon induction as finding a lexicon L that maximizes an informativeness coefficient, which measures the explanatory power of the lexicon beyond the information already contained in the confounders C. highly informative lexicons cannot simply collect words that reflect the confounds.