  Information provision experiments are a popular way to study causal effects
of beliefs on behavior. Researchers estimate these effects using TSLS. I show
that existing TSLS specifications do not estimate the average partial effect;
they have weights proportional to belief updating in the first-stage. If people
whose decisions depend on their beliefs gather information before the
experiment, the information treatment may shift beliefs more for people with
weak belief effects. This attenuates TSLS estimates. I propose researchers use
a local-least-squares (LLS) estimator that I show consistently estimates the
average partial effect (APE) under Bayesian updating, and apply it to Settele
(2022).
