3333 - Causal Inference - 2020

3333 - Causal Inference - 2020

Issued by GAIn® - The Global AI network

You will be taken on a full learning journey, beginning with the importance and value of causal relationships in business, answering the puzzling ‘what does this mean’ questions. We revise traditional statistics in order to make statistical claims about causality, using methods such as Randomized Trials and Instrumental Variables. We cover conceptual causal models and get hands-on experience performing estimation with do calculus, one of today’s most exciting and promising ideas in this field.

Skills

Earning Criteria

  • Importance: Able to explain the need and value of identifying causal relationships in business.
  • Traditional statistics: Understanding the limitations and shortcomings of traditional statistical and machine learning methods when aiming for causal claims.
  • Revisioning traditional statistics for causality: Being able to recognize when instrumental variables should be used and how to apply them.
  • Conceptual causal models: Being able to construct conceptual causal models.
  • Estimation in causal models: Being able to use a causal framework like Structural Causal Model and to estimate intervention effects using do-calculus.
  • Exam.