Design Principles & Causal Inference

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While much of statistics and data sciences is framed around problems of prediction, Design Principles and Causal Inference for Data-Based Decision Making will cover basic concepts of statistical methods for inferring causal relationships from data, with a perspective rooted in a potential-outcomes framework.  Issues such as randomized trials, observational studies, confounding, selection bias, and internal/external validity will be covered in the context of standard and non-typical data structures. The overall goal of the course is to train learners on how to formally frame questions of causal inference, give an overview of basic methodological tools to answer such questions and, importantly, provide a framework for interrogating the causal validity of relationships learned from data.  The target audience for this course is someone with basic statistical skills who seeks training on how to use data to characterize the consequences of well-defined actions or decisions.

What You Will Learn

  • How to formalize causality with observed data
  • Common threats to causal validity
  • Non-typical data structures
  • Novel design strategies
  • Causal inference


  • What is “causal inference”
  • Potential outcomes
  • Regression
  • Standardization
  • Matching designs
  • Quasi-experimental designs

Estimated Effort

10-12 Hours/week

Course Availability

  • Fall 2021

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