In this episode, Erik explains what casual inference is, use cases for this type of analysis and why asking for reviews from other team members is important. He also dives into experimentation and how we can evolve our analysis.

About Erik Gregory
"I am a Senior Staff Data scientist with 10+ years of experience who currently works at Meta.  My strengths and focus in data science are experimentation and causal inference, data tools and automation, and social network analysis.  In my current and past positions I have worked on ecosystem analyses, equitable product development, ads targeting, climate analysis, and Biostatistics research.  

I grew up in the Sacramento area, and attended school at American River College and later CSU Sacramento.  I currently live in Los Angeles with my wife and our toddler, with a permanent remote worker arrangement.  I like to try to teach myself musical instruments, run competitively, and go to restaurants and coffee shops with my family."

Overview

This is a great episode for data scientists or individuals looking to get into the field and specialize in causal inference and experimentation. Erik defines causality and what is needed for this type of analysis. He talks through how a causality use case might be used. Erik shares how we evolve the experimentation process.

Social Handle:
LinkedIn: https://www.linkedin.com/in/erik-gregory-58569a38


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