  This paper presents novel methods and theories for estimation and inference
about parameters in econometric models using machine learning for nuisance
parameters estimation when data are dyadic. We propose a dyadic cross fitting
method to remove over-fitting biases under arbitrary dyadic dependence.
Together with the use of Neyman orthogonal scores, this novel cross fitting
method enables root-$n$ consistent estimation and inference robustly against
dyadic dependence. We illustrate an application of our general framework to
high-dimensional network link formation models. With this method applied to
empirical data of international economic networks, we reexamine determinants of
free trade agreements (FTA) viewed as links formed in the dyad composed of
world economies. We document that standard methods may lead to misleading
conclusions for numerous classic determinants of FTA formation due to biased
point estimates or standard errors which are too small.
