  We propose new confidence sets (CSs) for the regression discontinuity
parameter in fuzzy designs. Our CSs are based on local linear regression, and
are bias-aware, in the sense that they take possible bias explicitly into
account. Their construction shares similarities with that of Anderson-Rubin CSs
in exactly identified instrumental variable models, and thereby avoids issues
with "delta method" approximations that underlie most commonly used existing
inference methods for fuzzy regression discontinuity analysis. Our CSs are
asymptotically equivalent to existing procedures in canonical settings with
strong identification and a continuous running variable. However, due to their
particular construction they are also valid under a wide range of empirically
relevant conditions in which existing methods can fail, such as setups with
discrete running variables, donut designs, and weak identification.
