  TSCI implements treatment effect estimation from observational data under
invalid instruments in the R statistical computing environment. Existing
instrumental variable approaches rely on arguably strong and untestable
identification assumptions, which limits their practical application. TSCI does
not require the classical instrumental variable identification conditions and
is effective even if all instruments are invalid. TSCI implements a two-stage
algorithm. In the first stage, machine learning is used to cope with
nonlinearities and interactions in the treatment model. In the second stage, a
space to capture the instrument violations is selected in a data-adaptive way.
These violations are then projected out to estimate the treatment effect.
