Deconfounding and Causal Regularization for Stability and External Validity

Brad Efron, in his lecture at the occasion of receiving the International Prize in Statistics, brought up some fascinating thoughts on "prediction, estimation and attribution", with particular attention to the new "wide data era" which has entered statistics and data science more generally (Efron, 2019, 2020). Looking back almost 20 years ago, there has been a huge development in statistics since Leo Breiman's article "Statistical Modeling: The Two Cultures" (Breiman, 2001). Even more broadly, data science has become an emerging new field and profession. It deals with information extraction from data, often in close proximity with other sciences. Its historical roots are in statistics, and statistical "critical" thinking plays an ever important role in inference from data to models and prediction. There are many interesting facets of this broad topic, see for example David Donoho's "50 years of Data Science" (Donoho, 2017) or Bin Yu's "Veridical Data Science" (Yu and Kumbier, 2020). Efron (2019, 2020) has formulated intriguing ideas on "prediction, estimation and attribution". We are presenting here a few additional considerations on the topic, as outlined in the following Sections 1.1 and 1.2.