The ability to quickly identify and resolve breakages among interconnected microservices is critical for any tech organization running production software. Unfortunately, in most organizations, identifying the root cause of a breakage can take engineers hours of manually sifting through logs and dashboards. In this talk, we described a fast, automated, and holistic approach to root cause analysis via an ensemble of structural causal models. This talk should be relevant to anyone interested in causal modeling, the field of observability, reliability engineering, or anyone wanting to troubleshoot production software issues faster.
YouTube Slides GitHub Repository DetailsThis tutorial session was intended to give attendees a gentle introduction to applying causal thinking and causal inference to data using python. Causal data analysis is very common in many academic domains (e.g. in social psychology, epidemiology, macroeconomics, public policy research, sociology, and more) as well as in industry (all of the largest Silicon Valley tech companies employ teams of scientists who answer business questions purely with causal inference methods). The tutorial involved a combination of a presentation with open Q&A and group exercises contained in Jupyter notebooks. Causal inference can be a very theory-heavy topic, making it impenetrable to novices. In this tutorial, I aimed to take a more practical perspective on causal inference, while still occasionally touching on the theory.
YouTube Slides GitHub Repository DetailsThis tutorial session was intended to give attendees a gentle introduction to applying causal thinking and causal inference to data using python. Causal data analysis is very common in many academic domains (e.g. in social psychology, epidemiology, macroeconomics, public policy research, sociology, and more) as well as in industry (all of the largest Silicon Valley tech companies employ teams of scientists who answer business questions purely with causal inference methods). The tutorial involved a combination of a presentation with open Q&A and group exercises contained in Jupyter notebooks. Causal inference can be a very theory-heavy topic, making it impenetrable to novices. In this tutorial, I aimed to take a more practical perspective on causal inference, while still occasionally touching on the theory.
YouTube Slides GitHub Repository DetailsIt's common for machine learning practitioners to train a supervised learning model, generate feature importance metrics, and then attempt to use these values to tell a data story that suggests what interventions should be taken to drive the outcome variable a favorable way (e.g. "X was an important feature in our churn prediction model, so we should consider doing more X to reduce churn"). This simply does not work, and the idea that standard feature importance measures can be interpretted causally is one of data science's more enduring myths. In this session we talked through why this isn't the case, what feature importance is actually good for, and we'll give a brief overview of a simple causal feature importance approach: Meta Learners. This talk should be relevant to machine learning practitioners of any skill level that want to gain actionable, causal insights from their predictive models.
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