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In recent years, critics of online platforms have raised concerns about theability of recommendation algorithms to amplify problematic content, withpotentially radicalizing consequences. However, attempts to evaluate the effectof 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'sintentions. Here we propose a method that we call ``counterfactual bots'' tocausally estimate the role of algorithmic recommendations on the consumption ofhighly partisan content. By comparing bots that replicate real users'consumption patterns with ``counterfactual'' bots that follow rule-basedtrajectories, we show that, on average, relying exclusively on the recommenderresults in less partisan consumption, where the effect is most pronounced forheavy partisan consumers. Following a similar method, we also show that ifpartisan consumers switch to moderate content, YouTube's sidebar recommender``forgets'' their partisan preference within roughly 30 videos regardless oftheir prior history, while homepage recommendations shift more graduallytowards moderate content. Overall, our findings indicate that, at least sincethe algorithm changes that YouTube implemented in 2019, individual consumptionpatterns mostly reflect individual preferences, where algorithmicrecommendations play, if anything, a moderating role.

Causally estimating the effect of YouTube's recommender system using counterfactual bots