Financial services has always been an industry where accountability is non-negotiable. Every credit decision carries a paper ...

A 55-year-old living in a moderately polluted city may already be carrying the disease burden of someone several years older, ...

Trained biostatisticians play a central role in clinical science and public health. Brown’s online master’s in biostatistics ...

A novel study has revealed a link between extreme weather and the risk of cardiovascular disease among middle-aged and older ...

Nutritional epidemiology faces challenges like recall bias and confounding. Advances in AI, image recognition, and ...

Causality-Benchmark is a library developed by IBM Research Haifa for benchmarking algorithms that estimate the causal effect of a treatment on some outcome. The framework includes unlabeled data, ...

This workshop offers a hands-on introduction for social scientists seeking to apply causal inference methods to observational and secondary data. Following a recap of foundational concepts of causal ...

Abstract: Causal inference with spatial, temporal, and meta-analytic data commonly defaults to regression modeling. While widely accepted, such regression approaches can suffer from model ...

Faculty develop methods for structured and unstructured biomedical data that advance statistical inference, machine learning, causal inference, and algorithmic modeling. Their work delivers principled ...

Background: Traditional congenital heart surgery quality assessments rely on indirect standardization via regression, which can be complicated by heterogeneity in case-mix, surgical volume, and low ...