Predicting Is Not Explaining: Targeted Learning of the Dative Alternation

Abstract Corpus linguists dig into large-scale collections of texts to better understand the rules governing a given language. We advocate for ambitious corpus linguistics drawing inspiration from the latest developments of semiparametrics for a modern targeted learning. Transgressing discipline-specific borders, we adapt an approach that has proven successful in biostatistics and apply it to the well-travelled case study of the dative alternation in English. A dative alternation is characterized by sentence pairs with the same verb, but different syntactic patterns, e.g. I gave a book to him (prepositional dative) and I gave him a book (double-object dative). Our aim is to explain how native speakers of English choose a pattern over another in any given context. The essence of the approach hinges on causal analysis and targeted minimum loss estimation (TMLE). Through causal analysis, we operationalize the set of scientific questions that we wish to address regarding the dative alternation. Drawing on the philosophy of TMLE, we answer these questions by targeting some versatile machine learners. We derive estimates and confidence regions for well-defined parameters that can be interpreted as the influence of each contextual variable on the outcome of the alternation (prepositional vs . double-object), all other things being equal.