text:   Topics",7,"['New paper from me, @rina_friedberg, and @BaiocchiMike: ""Robust Designs for Prospective Randomized Trials Surveying Sensitive Topics"". <LINK>', 'This work emerged out of our experience analyzing a cluster-randomized trial of an empowerment training program deployed to adolescent girls in Nairobi, Kenya. The treatment was intended to reduce the incidence of gender-based violence.', 'When surveying sensitive topics, reporting biases -- e.g. the possibility of underreporting troubling outcomes -- pose a threat to causal inference. We approach the problem under the potential outcomes framework, assuming a binary outcome.', 'We suppose reporting behavior is fixed given the choice of survey and show the joint distribution of ""reporting classes"" (e.g. underreporter, overreporter, truth-teller) and ""response classes"" (e.g. outcome increases, decreases, stays the same) determines the bias exactly. https://t.co/saUrrRg92u', 'Then, we propose a sensitivity model and an optimization procedure to determine the required sample size for achieving a desired power level, given the worst-case configuration of misreporters. https://t.co/mCsRmEWxcK', 'This is a challenging area! Insights from social scientists + local stakeholders are crucial to design the best survey instruments. Rigorous practices must be followed to preserve comfort and anonymity. Then, statisticians can design procedures to help address residual biases.', 'We hope folks find these results interesting and welcome any feedback!']",21,08,1448