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stat.ML2026
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…
stat.ML2026
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…