Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment
outcomes including efficacy and toxicity, thereby supporting the assessment and safety of …

The rise of internet-based services and products in the late 1990s brought about an
unprecedented opportunity for online businesses to engage in large scale data-driven …

An introduction to the emerging fusion of machine learning and causal inference. The book
presents ideas from classical structural equation models (SEMs) and their modern AI …

This article introduces GeoShapley, a game theory approach to measuring spatial effects in
machine learning models. GeoShapley extends the Nobel Prize–winning Shapley value …

In recent years, microeconometrics experienced the 'credibility revolution', culminating in the
2021 Nobel prices for David Card, Josh Angrist, and Guido Imbens. This 'revolution'in how to …

Causal inference from observational data is crucial for many disciplines such as medicine
and economics. However, sharp bounds for causal effects under relaxations of the …

By incorporating novel social media data, we analyze in detail how US companies offer
different employee benefits and how they are associated with several company performance …

We propose causal isotonic calibration, a novel nonparametric method for calibrating
predictors of heterogeneous treatment effects. Furthermore, we introduce cross-calibration, a …

Identifying causal effects from observational data is a pervasive challenge found throughout
the empirical sciences. Very general methods have been developed to decide the …

Background Due to the heterogeneity of low‐grade gliomas (LGGs), the lack of randomized
control trials, and strong clinical evidence, the effect of the extent of resection (EOR) is …