Large Language Models (LLMs) went from non-existent to ubiquitous in the machine
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …

Recent years have seen a rapid increase in the use of behavioural science to address the
priorities of public and private sector actors. There is now a vibrant ecosystem of …

Causality is a complex concept, which roots its developments across several fields, such as
statistics, economics, epidemiology, computer science, and philosophy. In recent years, the …

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

Causal inference is a critical research topic across many domains, such as statistics,
computer science, education, public policy, and economics, for decades. Nowadays …

Racism. Sexism. Heterosexism. Gender binarism. Together, they comprise intimately
harmful, distinct, and entangled societal systems of self-serving domination and privilege …

We discuss the relevance of the recent machine learning (ML) literature for economics and
econometrics. First we discuss the differences in goals, methods, and settings between the …

We apply causal forests to a dataset derived from the National Study of Learning Mindsets,
and discusses resulting practical and conceptual challenges. This note will appear in an …

Flexible estimation of heterogeneous treatment effects lies at the heart of many statistical
applications, such as personalized medicine and optimal resource allocation. In this article …

This paper provides an assessment of the early contributions of machine learning to
economics, as well as predictions about its future contributions. It begins by briefly …