Channel: PyData

Category: Science & Technology

Tags: pythonlearn to codeeducationsoftwarepydatalearncodinghow to programjuliaopensourcescientific programmingnumfocuspython 3tutorial

Description: Sliding into Causal Inference, with Python! Speaker: Alon Nir Summary What would the world look like if Russia had won the cold war? If the Boston Tea Party never happened? And where would we all be if Guido van Rossum had decided to pursue a career in theatre? While we can't slide into parallel worlds to explore alternative histories, we can simulate parallel realities (with Python, of course!) and give decent answers to intriguing 'what if' questions. Description What would the world look like if Russia had won the cold war? If the Boston Tea Party never happened? And where would we all be if Guido van Rossum had decided to pursue a career in theatre? Unfortunately we don't have the technology to slide into parallel worlds and explore alternative histories. However, it turns out we do have the tools to simulate parallel realities and give decent answers to intriguing 'what if' questions. This talk will provide a gentle introduction to these tools, or causal inference techniques. The talk is aimed at data practitioners, preferably with basic knowledge of Python and statistics. That said, the focus of the talk is to nurture an intuitive understanding of the subject first, and implementation in Python. By the end of the talk I hope audience members could identify causal inference problems, have an intuitive understanding of the different tools they can apply to these problems, and have the appetite to further their learning! Outline: Introduction Introduction to parallel universes and "what if?" questions? [2 mins] The golden standard for causal inference. We'll discuss randomised controlled experiments and also set the scene for cases these aren't possible. [6 mins] Main Selection bias and propensity score methods [8 mins] Synthetic Controls (or: creating an alternate universe on your machine) [8 mins] Recap What we saw (quick recap of when causal problems emerge and how to address them) [1 min] What we didn't see (a few words about other techniques, DAGs, etc.) [1 min] Quick overview of Python tools for causal inference [2 mins] Where do we go from here - resources, curriculums, readings and communities.[2 mins] Alon Nir's Bio Senior data scientist at Spotify 🎧. A versatile nerd. GitHub: github.com/alonnir Twitter: twitter.com/alonnir LinkedIn: linkedin.com/in/alonnir PyData Global 2021 Website: pydata.org/global2021 LinkedIn: linkedin.com/company/pydata-global Twitter: twitter.com/PyData 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: github.com/numfocus/YouTubeVideoTimestamps