In this talk I will give concise review of the major approaches found in academic literature and online resources for the purpose of inferring and detecting causality in time series data. I will start with motivation, explaining why detecting causality is important, the many different use cases it has, and why it cannot be done intuitively (correlation does not imply causation). I will then briefly go over the main theoretical approaches suggested over the years to define causality, highlighting the way they differ and the impact they have. Moving on, I will present the prominent approaches to infer causality, born of the previous definitions, focusing on limitations and pitfalls and almost always referring to Python or R implementations of each approach. Finally, I will give a short guide to which approach to choose, depending on your data, research question, possible assumptions and KPIs. About the speaker: I ❤️ learning, data science-ing and making open source Python. I’ve the NLPH initiative and co-founded the ML-centric hackathon DataHack and DataTalks meetup series. I work as a data science consultant. www.pydata.org PyData is an educational program of NumFOCUS, a 501 © 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.

#data analytic