Causal Inference

Are you seeking the next big thing in artificial intelligence? This two-day Causal Inference course will advance your skills past traditional statistical methods to make stronger causal claims. Providing a thorough investigation into how causal inference techniques can transform your business actions.

Duration: 2 day

Star Level: Master

Certification: Yes

 

MIacademy / Badge Overview / Open Course Calendar / 1201-Structured Opportunity Identification

Course Description

When using data analyses to steer decision-making, we want our actions to have the desired causal effect. And we all know that correlation does not imply causation. However, most techniques are limited to an associative, rather than a causal relationship. This training will broaden your analytical toolkit with different causal inference techniques. You will be taken on a full learning journey, beginning on the first day 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. On the second day, 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. After this training, you will be able to assess causal effects and become more critical towards other analysis conclusions and claims about causality, which will lead to a greater impact with your analyses and business actions.

 

Why is this for you?

 

In business we often want to know the effect of certain actions so we can take the best approach when making decisions. However, traditional statistics fail to give causal answers: drivers in regression are not causal, machine learning is just curve-fitting. Therefore, this causal inference course is crucial. It will allow you to translate real-world problems into a structural form and, by creating a causal model, estimate the effect of business interventions.

 

 

Who should attend?

 

This training is perfect for Data Scientists or Data Engineers looking to formulate causal inference skills and put into practice theoretical knowledge for useful business applications. To make the step up to causality a strong statistical and mathematical background is required. Participants must also have previously conquered badges: 3201 Machine Learning Process, 3202 Classification Using Tree Models, and 3203 Regression Models.

 

 

What will you learn?

 

This training will work with the concept of causal inference through the stags of revisioning, conceptual models, and estimation. Specifically, these three stages will include:

  1. Creating a directed acyclic graph
  2. Curve fitting
  3. Distilling testable implications from a causal model
  4. Using instrumental variables
  5. Applying randomized trials and treatment effects
  6. Constructing a Directed Acyclic Graph (DAG)
  7. Using a Causal Lift package in Python
  8. Estimating intervention effects using do calculus

 

Learning Goals

 

    • 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

 

Theory and practical use

 

All trainings in the GAIn portfolio combine high-quality standardized training material with theory sessions from experts and hands-on experience where you directly apply the material to real-life cases. Each training is developed by top of the field practitioners which means they are full of industry examples along with practical challenges and know-how, fueling the interactive discussions during training. We believe this multi-level approach creates the ideal learning environment for participants to thrive.

 

 

Skills

 

    • Machine Learning
    • Causal Inference
    • Statistics
    • Python
    • Do Calculus
    • Directed Acyclic Graph
Interested in taking the course?

Open Course Schedule

 

MIacademy does offer part of its portfolio in an Open Course Schedule Format in our location in the center of Amsterdam. Via the form below you can register your interest to participate. Our team will contact you to finalize the booking and answer any questions you may have.

All of our courses are delivered by our expert trainers.

 

If no dates are mentioned. The specific course is not scheduled yet in 2020. If this is the case you can use the form to register your interest. In case there is enough demand MIacademy can schedule additional courses and will notify you.

Dates & Availability

 

In-company Training Programs

Are you are interested to train a larger group of people, looking for specific training pe or and/or interested in creating a company-wide program? We will be happy to assist!

Whether you have a very specific training need (for example: training your Data Engineers on advanced technical topics, or your Data Scientists on model implementation), or the need for a large transformational program, or something in between, we can help. Over the past 13 years, we have built up extensive experience not only in the implementation of multi-year, multi-population, multi-country programs but also in providing high quality, very specific modules for specific target groups. Both in in-house set-ups and cross-company programs. Not sure what type of program would fit your organization best? We’d be happy to discuss the best approach together.