Political Analysis, Vol. 26, No. 1 (January 2018), pp. 54-71 (18 pages) Measuring the causal impact of state behavior on outcomes is one of the biggest methodological challenges in the field of ...

Our foray into causal analysis is not yet complete. Until we define the methods of causal inference, we can't get to the deeper insights that causal analysis can provide. This article details many of ...

Yang, Mochen, Edward McFowland III, Gordon Burtch, and Gediminas Adomavicius. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach ...

We know that correlation does not imply causation, but careful analyses of correlations are often our only way to quantify cause and effect in domains ranging from healthcare to education. This ...

When we talk about the limits of data science, we often revert to issues like scalability, or the lack of talent. But there's another burning question that data science projects overlook at their ...

Harvard University is providing seven free online courses in data science, each running for eight to nine weeks and requiring one to two hours of study per week. Applications are open until June 17, ...

This course offers a rigorous yet practical exploration of Bayesian reasoning for data-driven inference and decision-making. Students will gain a deep understanding of probabilistic modeling, and ...