Perelman School of Medicine at the University of Pennsylvania

Causal Inference and Big Data Summer Institute (CBD)

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July 24-27, 2017

The Causal Inference and Big Data Summer Institute is a four-day, intensive learning experience. Each day offers lectures by experts in the field, followed by live demonstrations of data analysis. Participants who bring laptops will have the opportunity to implement the methods during the computer lab sessions. Prerequisites include familiarity with traditional data analysis methods (such as regression models) and the programming language R. The institute is aimed at practitioners in industry, researchers, and students who are interested in learning about these statistical methods and how to implement them in practice.

While the first two days focus on causal inference and the second two days focus on big data, there is cross-over. For example, day 2 of causal inference includes machine learning methods in causal inference, and day 2 of big data includes a session on high dimensional causal inference.

The institute will take place at the University of Pennsylvania in Philadelphia.

Schedule Overview

Causal Day 1 (July 24)

Core topics: potential outcomes; DAGs; propensity scores; matching; instrumental variables

Causal Day 2 (July 25)

Advanced: time-dependent confounding; static and dynamic treatment regimes; marginal structural models; targeted maximum likelihood

Big Data Day 1 (July 26)

Core topics: supervised and unsupervised learning 

Big Data Day 2 (July 27)

Advanced: precision medicine; EHR; mobile health

The Institute is sponsored by the Center for Causal Inference and Center for Statistical Methods in Big Data