Get the latest news, data, and insights from research and industry experts.
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
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.
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