1 citations · 2 across the 7 of their papers we have counts for
7 papers
Deconfounding Scores and Representation Learning for Causal Effect Estimation with Weak Overlap
Oscar Clivio, Alexander D'Amour, Alexander Franks +3
Overlap, also known as positivity, is a key condition for causal treatment effect estimation. Many popular estimators suffer from high variance and become brittle when features dif…
Two Approaches to Direct Estimation of Riesz Representers
David Bruns-Smith
The Riesz representer is a central object in semiparametric statistics and debiased/doubly-robust estimation. Two literatures in econometrics have highlighted the role for directly…
Ridge Boosting is Both Robust and Efficient
David Bruns-Smith, Zhongming Xie, Avi Feller
Estimators in statistics and machine learning must typically trade off between efficiency, having low variance for a fixed target, and distributional robustness, such as multiaccur…
Augmented balancing weights as linear regression
David Bruns-Smith, Oliver Dukes, Avi Feller +1
We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning (AutoDML). These popular doubly robust or de-biased machine le…
Robust Fitted-Q-Evaluation and Iteration under Sequentially Exogenous Unobserved Confounders
David Bruns-Smith, Angela Zhou
Offline reinforcement learning is important in domains such as medicine, economics, and e-commerce where online experimentation is costly, dangerous or unethical, and where the tru…
Model-Free and Model-Based Policy Evaluation when Causality is Uncertain
David Bruns-Smith
When decision-makers can directly intervene, policy evaluation algorithms give valid causal estimates. In off-policy evaluation (OPE), there may exist unobserved variables that bot…
Outcome Assumptions and Duality Theory for Balancing Weights
David Bruns-Smith, Avi Feller
We study balancing weight estimators, which reweight outcomes from a source population to estimate missing outcomes in a target population. These estimators minimize the worst-case…