Against Machine Learning; For Causal Inference by Sarah Catanzaro Visit https://rstats.ai/nyr/ to learn more. Abstract: Nearly every day, data teams and venture capitalists implicitly express their priorities and outlook by making decisions on how to allocate budget or capital to advance different technology initiatives. In the past 5 years, both groups have prioritized machine learning and business intelligence initiatives, by investing the tools and platforms to support these projects. They have not; however, invested in tools and platforms to advance causal inference. In this talk, we will discuss why investments in causal inference may have a higher ROI. We’ll then study the evolution of the MLOps stack to identify opportunities to unlock increased investment in causal inference and expand adoption in industry. Bio: Sarah Catanzaro is a Partner at Amplify Partners, where she focuses on investing in and advising high potential startups in machine intelligence, data management, and distributed systems. Her investments at Amplify include startups like RunwayML, Maze Design, OctoML, and Metaphor Data among others. Sarah also has several years of experience defining data strategy and leading data science teams at startups and in the defense/intelligence sector including through roles at Mattermark, Palantir, Cyveillance, and the Center for Advanced Defense Studies. Twitter: https://twitter.com/sarahcat21 Presented at the 2021 New York R Conference (September 9, 2021)