Causal Targeting: Outcome Predictive Vs. Treatment-Effect Estimation

Foster Provost, professor of data science, professor of information systems and Andre Meyer Faculty Fellow at New York University Stern School of Business, will speak on “Causal Targeting: Outcome Predictive Vs. Treatment-Effect Estimation” on Thursday, April 19 from 12:45–1:45 p.m. in Stuart Building, Room 111.

Provost will present the results of a theoretical analysis and supporting simulation analysis comparing treatment effect estimation vs. simple outcome prediction when addressing causal classification. Using outcome prediction may be preferable to treatment effect estimation, even when the best possible models are used for both approaches and there are no estimation challenges (such as confounding). Specifically, outcome prediction is preferable when positive outcomes are (1) very rare, (2) difficult to predict, and when (3) treatment effects are small.

Provost is former director of NYU’s Center for Data Science and former editor-in-chief of the journal Machine Learning, and he was elected as a founding board member of the International Machine Learning Society. His book Data Science for Business is a perennial bestseller. He has a B.S. from Duquesne University and a Ph.D. from the University of Pittsburgh.

Event Topic

Distinguished Lecture Series