The Data-Fusion Problem: Causal Inference and Reinforcement Learning

When:
Friday, February 3, 2017, 11:00 am - 12:00 pm PSTiCal
Where:
11th floor large conference room
This event is open to the public.
Type:
AI SEMINAR
Speaker:
Elias Bareinboim
Description:

Machine Learning is usually dichotomized into two categories, passive (e.g., supervised learning) and active (e.g., reinforcement learning) which, by and large, are studied separately. Reality is more demanding. Passive and active modes of operation are but two extremes of a rich spectrum of data-collection modes (also called research designs) that generate the bulk of the data available in practical, large scale situations. In typical medical explorations, for example, data from multiple observations and experiments are collected, coming from distinct experimental setups, different sampling conditions, and heterogeneous populations. Similarly, in a more basic setting, a baby learns from its environment by both passively observing others and interacting with its environment by actively performing interventions. In this task, I will review the theory of structural causality and use it to explain the relationship between causal inference and reinforcement learning (RL). Further, I will formulate and discuss a collection of inference tasks that lie in the intersection of RL and causal inference, including personalized decision-making.

 

Bio: 

 Elias Bareinboim is an assistant professor in the Department of Computer Science at Purdue University. His research focuses on causal and counterfactual inference and their applications to data-driven fields. Bareinboim received a Ph.D. in Computer Science from UCLA advised by Judea Pearl. His doctoral thesis was the first to propose a general solution to the problem of “data-fusion” and to provide practical methods for combining datasets generated under different experimental conditions. Bareinboim’s recognitions include IEEE AI’s 10 to Watch, the Dan David Prize Scholarship, the Yahoo! Key Scientific Challenges Award, and the 2014 AAAI Outstanding Paper Award.

 

 

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