causalscience.org – Newsletter
May 21, 2021
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.
April 27, 2021
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 26, 2021
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.
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