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Data Science

Why and What If – Causal Analysis for Everyone

Published by Pearson

Intermediate content levelIntermediate

Online Experiments, Bayesian Networks, Causal Models and Interventions

How do causes lead to effects? Can you associate the cause leading to the observed effect? Big Data opens the doors for us to be able to answer questions such as this, but before we are able to do so, we must go beyond classical probability theory and dive into the field of Causal Inference.

In this course, we will explore the three steps in the ladder of causation: 1. Association 2. Intervention 3. Counterfactuals with simple rules and techniques to move up the ladder from simple correlational studies to fully causal analyses. We will cover the fundamentals of this powerful set of techniques allowing us to answer practical causal questions such as “Does A cause B?” and “If I change A how does that impact B?”

What you’ll learn and how you can apply it

  • Understand the fundamental difference between correlation and causation
  • Be able to identify and handle confounders
  • Build and evaluate simple causal models
  • Adopt a causal frame of mind
  • Combine Machine Learning and Causal approaches

This live event is for you because...

  • You’re a data scientist who wants to go beyond association analyses to answer causal questions
  • You want to be able to identify the causal mechanisms at work in your data
  • You want to take advantage of causal structure to improve your understanding of your dataset and speed you your computations

Prerequisites

  • Basic Python
  • Jupyter

Course Set-up

  • Scientific Python distribution like Anaconda

Recommended Preparation

Recommended Follow-up

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

Segment 1 – Approaches to Causality (55 min)

  • Probability Theory
  • Simpsons Paradox
  • A/B Testing
  • Granger Causality
  • Graphical Models
  • The Ladder of Causality

Break (10 min)

Segment 2 – Properties of Graphical models (50 min)

  • Chains
  • Forks
  • Colliders
  • d-separation

Break (10 min)

Segment 3 – Interventions (50 min)

  • Interventions
  • Back-door criterion
  • Front-door criterion
  • Mediation

Break (10 min)

Segment 4 – Counterfactuals (30 min)

  • The fundamental laws of counterfactuals
  • Graphical representation
  • Practical Applications

Break (5 min)

Segment 5 – Connections to Machine Learning (30 min)

  • Structure Identifiability
  • Semi-Supervised learning
  • Applications to time-series

Your Instructor

  • Bruno Gonçalves

    Bruno Gonçalves is currently a Head of Data Science working at the intersection of AI, Blockchain Technologies, and Finance. Previously, he was a Data Science Fellow at NYU's Center for Data Science while on leave from a tenured faculty position at Aix-Marseille Université. Since the completion of his PhD in the Physics of Complex Systems in 2008, he has pursued the use of Data Science and Machine Learning to the large-scale study of human behavior. In 2015, he was awarded the Complex Systems Society's Junior Scientific Award for "outstanding contributions in Complex Systems Science," and in 2018 he was named a Science Fellow of the Institute for Scientific Interchange in Turin, Italy.

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