  Open-Domain Question Answering (ODQA) systems necessitate a reader model
capable of generating answers by simultaneously referring to multiple passages.
Although representative models like Fusion-in-Decoder (FiD) have been proposed
to address this challenge, these systems can inadvertently rely on spurious
features instead of genuine causal relationships between the question and the
passages to generate answers. To counter this problem, we introduce the
Rational Fusion-in-Decoder (RFiD) model. Our model leverages the encoders of
FiD to differentiate between causal relationships and spurious features,
subsequently guiding the decoder to generate answers informed by this
discernment. Experimental results on two ODQA datasets, Natural Questions (NQ)
and TriviaQA (TQ), demonstrate that our model surpasses previous methods,
achieving improvements of up to 1.5 and 0.7 in Exact Match scores on NQ, and
exhibits an enhanced ability to identify causal relationships.
