Information-theoretic causal inference of lexical flow

This thesis starts out with the idea of modeling human languages as variables, and proceeds to define a conditional independence relation between the core vocabularies of languages. The conditional independence relationships are then used to infer two types of directed networks over languages which have all the properties of causal graphs. Such a graph can be interpreted as a parsimonious explanation of how the lexicon of the investigated languages was shaped by inheritance and contact. Many preparatory steps were necessary to arrive at good test data for the new methods. Since none of the existing lexical databases has all the characteristics necessary for automatic computation of lexical overlaps across language family boundaries, considerable effort was put into compiling a new lexical database of Northern Eurasia from dictionary data. This database contains data for an unusually large list of more than a thousand concepts, and is the first database to cover the languages of a large continuous geographic area with more than 20 language families in a unified phonetic format. In this thesis, the phonetic forms are clustered into approximate cognate sets by means of information-weighted sequence alignment. This new alignment method makes it possible to refine established methods for automated cognacy detection in such a way that they work on dictionary forms, making it unnecessary to manually reduce all words to their stems before running cognate detection on them. The core part of this thesis starts by turning the cognacy overlaps into a consistent information measure for sets of languages. This results in a conditional mutual information measure which quantifies a notion of lexical flow, where the lexical material needs to be distributed via paths connecting languages to explain their overlap. Standard causal inference algorithms are then applied to conditional independence constraints arising from vanishing mutual information. In phylogenetic lexical flow inference (PLFI), ancestral state reconstruction methods from bioinformatics are used to reconstruct cognates for the unattested proto-languages, which become part of the flow model as sources of lexical overlaps between related languages. In contrast, contact lexical flow inference (CLFI) does not explicitly model the proto-languages, which then act as latent confounders causing spurious dependencies. This is not necessarily a problem for causal inference, because some advanced causal inference algorithms can distinguish such dependence relations from those caused by a direct causal relationship. In both variants, the discrete and unrealiable nature of the cognate data makes it necessary to develop alternative methods for the different steps of causal inference, with the purpose of increasing robustness against erroneous cognacy judgments. Both methods are evaluated on the lexical database as well as large amounts of simulated cognacy data. The model used to generate the simulated data is based on a simple evolutionary model which models language change by lexical replacement and borrowing on the level of indidual words. The model is shown to produce realistic cognate data which will also be of use in validating other methods for inferring evolutionary networks from cognacy-encoded language data.