Algorithmizing Counterfactual Reasoning
How to write algorithms that handle advanced causal counterfactuals
Watch PromoYour Instructor
Robert is currently a research scientist at Microsoft Research and faculty at the Northeastern University department of computer science. He has worked in industry as a research engineer and data scientist building production quality systems for Bayesian decision-making under uncertainty.
Robert didn't start in machine learning. He started his career by becoming fluent in Mandarin Chinese and moving to Tibet to do developmental economics fieldwork. He later obtained a graduate degree from Johns Hopkins School of Advanced International Studies.
Robert attained his Ph. D. in mathematical statistics from Purdue University. He has published in top tier journals and venues on topics including causal inference, probabilistic modeling, sequential decision processes, and dynamic models of complex systems.
Course Curriculum
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StartIntroduction to Counterfactual Reasoning (3:46)
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StartThe Twin-World Counterfactual Inference Algorithm (9:18)
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StartAssessment - Counterfactuals
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StartMediation: An Algorithmic Bias Case Study
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StartSingle World Counterfactuals & Effect of Treatment on the Treated
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StartProbabilities of Causation
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StartMechanistic Inductive Bias for Multiverse Counterfactuals
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StartAssessment - Necessity and Sufficiency