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Today we close out our 2021 ICML series joined by Lina Montoya, a postdoctoral researcher at UNC Chapel Hill.
In our conversation with Lina, who was an invited speaker at the Neglected Assumptions in Causal Inference Workshop, we explored her work applying Optimal Dynamic Treatment (ODT) to understand which kinds of individuals respond best to specific interventions in the US criminal justice system. We discuss the concept of neglected assumptions and how it connects to ODT rule estimation, as well as a breakdown of the causal roadmap, coined by researchers at UC Berkeley.
Finally, Lina talks us through the roadmap while applying the ODT rule problem, how she's applied a "superlearner" algorithm to this problem, how it was trained, and what the future of this research looks like.
SigOpt was born out of the desire to make experts more efficient. While co-founder Scott Clark was completing his PhD at Cornell he noticed that often the final stage of research was a domain expert tweaking what they had built via trial and error. After completing his PhD, Scott developed MOE to solve this problem, and used it to optimize machine learning models and A/B tests at Yelp. SigOpt was founded in 2014 to bring this technology to every expert in every field.