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

<p dir="ltr">This journal contribution is published Open Access by the publisher. Follow the DOI link to retrieve a copy of the full text. </p><p dir="ltr">We are grateful to the Nielsen Company for access to their desktop panel data and to B. Sissenich, S. Sherman, H. Baberwal, and E. Grimaldi for ongoing support. Additionally, H.H., M.R.-L., and D.J.W. are grateful for the financial support provided by Richard Jay Mack and the Carnegie Corporation of New York (Grant G-F-20-57741). A.G. is supported by the NSF under Grant No. 2030859 to the Computing Research Association for the CIFellows Project. M.H.R. and R.W. acknowledge support from the Swiss NSF (Grant 200021_185043) and the European Union (TAILOR, Grant 952215).,H.H., A.G., M.R.-L., M.H.R., R.W., and D.J.W. designed research; H.H. and A.G. performed research; H.H. and A.G. analyzed data; M.H.R. designed bots; and H.H. and D.J.W. wrote the paper.,The authors declare no competing interest.</p>