The use of Amazon's Mechanical Turk (MTurk) in management research has increased over
2,117% in recent years, from 6 papers in 2012 to 133 in 2019. Among scholars, though …

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 …

The ability to perform causal reasoning is widely considered a core feature of intelligence. In
this work, we investigate whether large language models (LLMs) can coherently reason …

The two fields of machine learning and graphical causality arose and are developed
separately. However, there is, now, cross-pollination and increasing interest in both fields to …

When assessing causal effects, determining the target population to which the results are
intended to generalize is a critical decision. Randomized and observational studies each …

We study causal representation learning, the task of inferring latent causal variables and
their causal relations from high-dimensional functions (“mixtures”) of the variables. Prior …

External validity captures the extent to which inferences drawn from a given study's sample
apply to a broader population or other target populations. Social scientists frequently invoke …

The machine learning community's interest in causality has significantly increased in recent
years. My understanding of causality has been shaped by Judea Pearl and a number of …

Big data, high-performance computing, and (deep) machine learning are increasingly
becoming key to precision medicine—from identifying disease risks and taking preventive …

Background Evidence, in multiple forms, is a foundation of implementation science. For
public health and clinical practice, evidence includes the following: type 1 evidence on …