Measurement Error Can Be a Serious Problem for Causal Inference

Measurement error is an important and often overlooked problem in (causal) data science. In this post, Daniel Millimet, from Southern Methodist University, explains what you have to watch out for if your analysis is based on mismeasured data.

May 21, 2021–Daniel Millimet

Dan Lohmar, Unsplash.

Erin Hervey, Unsplash.

A Smarter Way to Use the Strengths of Your Instrumental Variables

In this post, Nick Huntington-Klein explains how you can exploit variation in the effects of instrumental variables in different subsamples to improve the performance of your IV estimations.

April 27, 2021–Nick Huntington-Klein

Robert Bye, Unsplash.

How Can Causal Machine Learning Improve Business Decisions?

In this post, Margarita and Martin Huber explain why understanding the causal impact of particular business activities like marketing campaigns or pricing policies is necessary when making decisions about the appropriate design of those actions.

April 26, 2021–Margarita Huber,Martin Huber

What is Causal Data Fusion?

This post outlines the data fusion process. This automated and do-calculus based inferential engine opens up a complete causal inference pipeline, from problem specification, to identification, to estimation.

April 1, 2021–Paul Hünermund

Causal Data Science in Practice

Causal data science methods are currently experiencing growing adoption in industry. In spring 2020, we conducted a global survey of data science practitioners to explore the use of causal data science methods in business practice.

March 1, 2021–Carla Schmitt

Daryan Shamkhali, Unsplash. Williamsburg, New York.

Causal Inference is More than Fitting the Data Well

Causal inference tools can be challenging for practitioners to adopt because they require an entirely new approach to data science. This post describes some of the cultural and organizational challenges that come with this new paradigm.

Febuary 1, 2021–Paul Hünermund

Zalando HQ, Berlin.

How to Push Causal Inference in Industry?

Patrick Doupe, Principal Economist at Zalando SE, shares his team's experience about what it takes to raise attention to causal inference in practice.

December 17, 2020–Patrick Doupe

Thank you, everyone!

We are delighted to share a summary of the Causal Data Science Meeting 2020 with you. This year's event was jointly organized by Maastricht University and Copenhagen Business School.

November 19, 2020–Jermain Kaminski,Paul Hünermund,Carla Schmitt,Beyers Louw

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