430 citations · 512 across the 19 of their papers we have counts for
4 papers · 1 filter
Boosting the interpretability of clinical risk scores with intervention predictions
Eric Loreaux, Ke Yu, Jonas Kemp +8
Machine learning systems show significant promise for forecasting patient adverse events via risk scores. However, these risk scores implicitly encode assumptions about future inte…
Understanding the Risks and Rewards of Combining Unbiased and Possibly Biased Estimators, with Applications to Causal Inference
Michael Oberst, Alexander D'Amour, Minmin Chen +3
Several problems in statistics involve the combination of high-variance unbiased estimators with low-variance estimators that are only unbiased under strong assumptions. A notable…
Calibration Error for Heterogeneous Treatment Effects
Yizhe Xu, Steve Yadlowsky
Recently, many researchers have advanced data-driven methods for modeling heterogeneous treatment effects (HTEs). Even still, estimation of HTEs is a difficult task -- these method…
Explaining Practical Differences Between Treatment Effect Estimators with High Dimensional Asymptotics
Steve Yadlowsky
We revisit the classical causal inference problem of estimating the average treatment effect in the presence of fully observed confounding variables using two-stage semiparametric…