Abstract: Causal inference is one of the hardest parts about data science. Because not only does it require great technical expertise, but it also requires a practical understanding of the problem.
While understanding the entire field of causal inference would require a thorough undergraduate study if not a Ph.D, we data scientists can get a lot of value from understanding the most versatile tool in causal inference: the parametric G formula.
I'll be explaining this tool in simple English and code that will allow you to use it in your day-to-day job. I'll also point you to further reading or you can up your game in causal inference to tackle even more challenging tasks.
Background Knowledge
An introductory level of data analytics and python is all you'll need to attend this talk!
Bio: Nathaniel earned his AB/SM in Computer Science from Harvard. He previously worked as a Quant and Trader at Jane Street and Goldman Sachs before transitioning into the pure tech industry. Nathaniel worked as a Data Scientist at Facebook, a Product Manager at Microsoft and a Software Engineer at Google before joining Vicarious. He is an avid reader and learner. He teaches part time at General Assembly and is developing open source teaching material for data science, machine learning, and web development.
