1 citations · 2 across the 4 of their papers we have counts for
4 papers
Artificial intelligence surrogates for treatment effect estimation with before-and-after data
Frances Dean, Anna Neufeld, Joshua Barrios +2
Estimating the causal effects of medical treatments is difficult when clinically important outcomes are costly to measure or require long follow-up. Short-term or inexpensive surro…
Towards causal effect estimation with learned instrument representations
Frances Dean, Jenna Fields, Radhika Bhalerao +2
Instrumental variable (IV) methods mitigate bias from unobserved confounding in observational causal inference but rely on the availability of a valid instrument, which can often b…
Medical Large Language Model Benchmarks Should Prioritize Construct Validity
Ahmed Alaa, Thomas Hartvigsen, Niloufar Golchini +4
Medical large language models (LLMs) research often makes bold claims, from encoding clinical knowledge to reasoning like a physician. These claims are usually backed by evaluation…
Med-Real2Sim: Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning
Keying Kuang, Frances Dean, Jack B. Jedlicki +4
A digital twin is a virtual replica of a real-world physical phenomena that uses mathematical modeling to characterize and simulate its defining features. By constructing digital t…