Search Results for author: Steve Yadlowsky

Found 12 papers, 6 papers with code

Calibration Error for Heterogeneous Treatment Effects

1 code implementation24 Mar 2022 Yizhe Xu, Steve Yadlowsky

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.

Survival Analysis

Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects

no code implementations15 Nov 2021 Steve Yadlowsky, Scott Fleming, Nigam Shah, Emma Brunskill, Stefan Wager

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.

Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests

no code implementations NeurIPS 2021 Victor Veitch, Alexander D'Amour, Steve Yadlowsky, Jacob Eisenstein

We introduce counterfactual invariance as a formalization of the requirement that changing irrelevant parts of the input shouldn't change model predictions.

Causal Inference Text Classification

Counterfactual Invariance to Spurious Correlations in Text Classification

no code implementations NeurIPS 2021 Victor Veitch, Alexander D'Amour, Steve Yadlowsky, Jacob Eisenstein

We introduce counterfactual invariance as a formalization of the requirement that changing irrelevant parts of the input shouldn't change model predictions.

Causal Inference Text Classification

SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression

1 code implementation NeurIPS 2021 Steve Yadlowsky, Taedong Yun, Cory McLean, Alexander D'Amour

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

4 code implementations16 Jan 2021 Chirag Nagpal, Steve Yadlowsky, Negar Rostamzadeh, Katherine Heller

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.

Survival Analysis

Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding

1 code implementation NeurIPS 2020 Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky, Emma Brunskill

We assess robustness of OPE methods under unobserved confounding by developing worst-case bounds on the performance of an evaluation policy.

Decision Making

Estimation and Validation of Ratio-based Conditional Average Treatment Effects Using Observational Data

no code implementations15 Dec 2019 Steve Yadlowsky, Fabio Pellegrini, Federica Lionetto, Stefan Braune, Lu Tian

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

1 code implementation11 Apr 2018 Tatsunori B. Hashimoto, Steve Yadlowsky, John C. Duchi

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.

Active Learning General Classification

Adaptive Sampling Probabilities for Non-Smooth Optimization

no code implementations ICML 2017 Hongseok Namkoong, Aman Sinha, Steve Yadlowsky, John C. Duchi

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.

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