Amir Emad Ghassami

Amir Emad’s research focuses on causal inference and causal discovery. He develops statistical methodologies for evaluating the causal effect of actions or treatments on outcome variables of interest, designing policies for optimizing outcomes, and identifying the causal relationships among the variables of a system. He investigates such causal queries in the face of real-world data complexities such as ​presence of unobserved confounders, measurement error, and missing and censored values. The tools he uses in his research include methods from the modern semi- and non-parametric statistics, state-of-the-art machine learning, and information theory. 

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