430 citations · 444 across the 4 of their papers we have counts for
8 papers
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…
Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests
Victor Veitch, Alexander D'Amour, Steve Yadlowsky +1
Informally, a 'spurious correlation' is the dependence of a model on some aspect of the input data that an analyst thinks shouldn't matter. In machine learning, these have a know-i…
SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression
Steve Yadlowsky, Taedong Yun, Cory McLean +1
Logistic regression remains one of the most widely used tools in applied statistics, machine learning and data science. However, in moderately high-dimensional problems, where the…
Underspecification Presents Challenges for Credibility in Modern Machine Learning
Alexander D'Amour, Katherine Heller, Dan Moldovan +37
ML models often exhibit unexpectedly poor behavior when they are deployed in real-world domains. We identify underspecification as a key reason for these failures. An ML pipeline i…
Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding
Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky +1
When observed decisions depend only on observed features, off-policy policy evaluation (OPE) methods for sequential decision making problems can estimate the performance of evaluat…