Randomized controlled trials (RCTs) are considered the gold standard for estimating the
average treatment effect (ATE) of interventions. One use of RCTs is to study the causes of …

To combat poor health and living conditions, policymakers in Africa require temporally and
geographically granular data measuring economic well-being. Machine learning (ML) offers …

Using deep learning with satellite images enhances our understanding of human
development at a granular spatial and temporal level. Most studies have focused on Africa …

Two decades ago, Leo Breiman identified two cultures for statistical modeling. The data
modeling culture (DMC) refers to practices aiming to conduct statistical inference on one or …

In today's world, where the global population is expanding at an unprecedented rate,
addressing the challenge of poverty has become more critical than ever before. In the wake …

In this paper, we demonstrate how to enhance the validity of causal inference with
unstructured high-dimensional treatments like texts, by leveraging the power of generative …

The warming of the Arctic, also known as Arctic amplification, is led by several atmospheric
and oceanic drivers. However, the details of its underlying thermodynamic causes are still …

Many social and environmental phenomena are associated with macroscopic changes in
the built environment, captured by satellite imagery on a global scale and with daily …

Designing faithful yet accurate AI models is challenging, particularly in the field of individual
treatment effect estimation (ITE). ITE prediction models deployed in critical settings such as …

The concept of sustainable intensification in agriculture necessitates the implementation of
management practices that prioritize sustainability without compromising productivity …