Search Results for author:
Found 12 papers, 6 papers with code
Calibration Error for Heterogeneous Treatment Effects
However, while many methods exist for evaluating the calibration of prediction and classification models, formal approaches to assess the calibration of HTE models are limited to the calibration slope.
Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects
On the other hand, in a large marketing trial, we find robust evidence of heterogeneity in the treatment effects of some digital advertising campaigns and demonstrate how RATEs can be used to compare targeting rules that prioritize estimated risk vs. those that prioritize estimated treatment benefit.
The MultiBERTs: BERT Reproductions for Robustness Analysis
Experiments with pre-trained models such as BERT are often based on a single checkpoint.
Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests
We introduce counterfactual invariance as a formalization of the requirement that changing irrelevant parts of the input shouldn't change model predictions.
Counterfactual Invariance to Spurious Correlations in Text Classification
We introduce counterfactual invariance as a formalization of the requirement that changing irrelevant parts of the input shouldn't change model predictions.
SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression
The key insight of SLOE is that the Sur and Cand\`es (2019) correction can be reparameterized in terms of the \emph{corrupted signal strength}, which is only a function of the estimated parameters $\widehat \beta$.
Deep Cox Mixtures for Survival Regression
Survival analysis is a challenging variation of regression modeling because of the presence of censoring, where the outcome measurement is only partially known, due to, for example, loss to follow up.
Underspecification Presents Challenges for Credibility in Modern Machine Learning
Predictors returned by underspecified pipelines are often treated as equivalent based on their training domain performance, but we show here that such predictors can behave very differently in deployment domains.
Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding
We assess robustness of OPE methods under unobserved confounding by developing worst-case bounds on the performance of an evaluation policy.
Estimation and Validation of Ratio-based Conditional Average Treatment Effects Using Observational Data
Motivated by the need of modeling the number of relapses in multiple sclerosis patients, where the ratio of relapse rates is a natural choice of the treatment effect, we propose to estimate the conditional average treatment effect (CATE) as the ratio of expected potential outcomes, and derive a doubly robust estimator of this CATE in a semiparametric model of treatment-covariate interactions.
Derivative free optimization via repeated classification
We develop an algorithm for minimizing a function using $n$ batched function value measurements at each of $T$ rounds by using classifiers to identify a function's sublevel set.
Adaptive Sampling Probabilities for Non-Smooth Optimization
Standard forms of coordinate and stochastic gradient methods do not adapt to structure in data; their good behavior under random sampling is predicated on uniformity in data.
