  In recent years, critics of online platforms have raised concerns about the
ability of recommendation algorithms to amplify problematic content, with
potentially radicalizing consequences. However, attempts to evaluate the effect
of recommenders have suffered from a lack of appropriate counterfactuals --
what a user would have viewed in the absence of algorithmic recommendations --
and hence cannot disentangle the effects of the algorithm from a user's
intentions. Here we propose a method that we call ``counterfactual bots'' to
causally estimate the role of algorithmic recommendations on the consumption of
highly partisan content. By comparing bots that replicate real users'
consumption patterns with ``counterfactual'' bots that follow rule-based
trajectories, we show that, on average, relying exclusively on the recommender
results in less partisan consumption, where the effect is most pronounced for
heavy partisan consumers. Following a similar method, we also show that if
partisan consumers switch to moderate content, YouTube's sidebar recommender
``forgets'' their partisan preference within roughly 30 videos regardless of
their prior history, while homepage recommendations shift more gradually
towards moderate content. Overall, our findings indicate that, at least since
the algorithm changes that YouTube implemented in 2019, individual consumption
patterns mostly reflect individual preferences, where algorithmic
recommendations play, if anything, a moderating role.
