Causal Inference for Social Science (Stanford University)

Causal inference methods have revolutionized the way we use data, statistics, and research design to move from correlation to causation and rigorously learn about the impact of some potential cause (e.g., a new policy or intervention) on some outcome (e.g., election results, levels of violence, poverty). This course provides an introduction to the toolkit of modern causal inference methods as they are now widely used across academic felds, government, industry and non-profits. Topics include experiments, matching, regression, difference-in-differences, panel methods, instrumental variable estimation and regression discontinuity designs. We will illustrate and apply the methods with examples drawn from various fields including policy evaluation, political science, public health, economics, business and sociology.

Note: Slides adapted from previous iteration of this course designed and taught by Prof. Jens Hainmueller

 

Semester: 

Spring

Offered: 

2017

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