What to do When You Can't A/B test: Exploring Different Causal Inference Methods by Camelia Hssaine. Visit https://rstats.ai/nyr/ to learn more. Abstract: A/B testing is the gold standard for experimentation, but in practice randomization is not always feasible when certain biases are in play. It is common then for data scientists to leverage quasi-experimental methods to measure average treatment effects. In this talk we’ll go through examples of industry applications within ridesharing and edtech, where methods such as difference-in-differences, synthetic controls and counterfactual analysis and estimation were used to infer causality. Bio: Camelia Hssaine is a Data Scientist at Codecademy. Twitter: https://twitter.com/cameliacassetet Presented at the 2020 R Conference | New York (August 15th, 2020)