Blog

What can an age-old science fiction flick tell us about the quest for general AI? Explaining the ladder of causation at the example of C-3PO.

Posted February 21, 2023 by Dustin Frederik Rusteberg and Paul Hünermund ‐ 10 min read

We are delighted to share a summary of the Causal Data Science Meeting 2022 with you. The event was jointly organized by Maastricht University and Copenhagen Business School.

Posted November 24, 2022 by Paul Hünermund and Jermain Kaminski and Beyers Louw and Carla Schmitt ‐ 4 min read

The Causal Data Science Meeting 2022 (Nov 7–8, 2022) aims to establish an interdisciplinary dialogue between academics and business experts on causal inference methods & applications.

Posted September 1, 2022 by Paul Hünermund and Jermain Kaminski ‐ 2 min read

In this post, Ben Tengelsen from the IntelyCare Research Group explains how Bayesian structural time-series models and experimentation helped to improve a referral program.

Posted August 28, 2022 by Ben Tengelsen ‐ 8 min read

In this post, Brant Callaway from the University of Georgia explains why time-varying covariates might pose a problem in TWFE and what steps you can take to avoid them.

Posted March 15, 2022 by Brant Callaway ‐ 12 min read

Anders Bast Olsen, master student at Copenhagen Business School, explains how actionable business insights can be derived from directed acyclic graphs and why data analytics and qualitative approaches form a powerful combination for causal learning.

Posted March 14, 2022 by Anders Bast Olsen ‐ 7 min read

We are delighted to share a summary of the Causal Data Science Meeting 2021 with you. The event was jointly organized by Maastricht University and Copenhagen Business School.

Posted November 19, 2021 by Paul Hünermund and Jermain Kaminski and Carla Schmitt and Beyers Louw ‐ 3 min read

We see more and more examples of companies starting to invest in causal inference capabilities. But how widespread is this phenomenon? And who are the industry players that are active in this space?

Posted September 9, 2021 by Paul Hünermund and Jermain Kaminski ‐ 5 min read

We talked with Florent Buisson, consultant and author, about his new book 'Behavioral Data Analysis with R & Python' (O’Reilly) and his perspective on data science and teaching.

Posted August 30, 2021 by Paul Hünermund and Florent Buisson ‐ 5 min read

The Causal Data Science Meeting 2021 (Nov 15–16, 2021) aims to establish an interdisciplinary dialogue between academics and business experts on causal inference methods & applications.

Posted August 10, 2021 by Paul Hünermund and Jermain Kaminski ‐ 4 min read

In this post, Simon Grest, Data Science Lead at the Netherlands' largest food retailer Albert Heijn, shows how using bootstrapping with historical data can be used to validate different estimators in A/B testing.

Posted August 5, 2021 by Simon Grest ‐ 11 min read

Do you experiment with different models in production? Sam Weiss from Ibotta on how you can use observational causal inference to build a better model on data from previous model deployments.

Posted August 2, 2021 by Sam Weiss ‐ 11 min read

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.

Posted May 21, 2021 by Daniel Millimet ‐ 9 min read

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.

Posted April 27, 2021 by Nick Huntington-Klein ‐ 7 min read

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.

Posted April 26, 2021 by Margarita Huber and Martin Huber ‐ 6 min read

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.

Posted April 1, 2021 by Paul Hünermund ‐ 5 min read

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.

Posted March 1, 2021 by Carla Schmitt ‐ 6 min read

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.

Posted February 1, 2021 by Paul Hünermund ‐ 4 min read

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

Posted December 17, 2020 by Patrick Doupe ‐ 7 min read