Michael Johns: Propensity Score Matching: A Non-experimental Approach to Causal... | PyData NYC 2019 скачать в хорошем качестве

Michael Johns: Propensity Score Matching: A Non-experimental Approach to Causal... | PyData NYC 2019

Full title: Michael Johns: Propensity Score Matching: A Non-experimental Approach to Causal Inference | PyData New York 2019 Propensity score matching provides an alternative framework for causal inference when random assignment is not possible. The technique draws on core data science skills of predictive model building and algorithm development. Data scientists who need alternatives to experiments will find this a useful and accessible addition to their methodological toolbox. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVi...