Search Results for author:
Found 13 papers, 5 papers with code
Causal Inference for Genomic Data with Multiple Heterogeneous Outcomes
In this paper, we propose a generic semiparametric inference framework for doubly robust estimation with multiple derived outcomes, which also encompasses the usual setting of multiple outcomes when the response of each unit is available.
Double Cross-fit Doubly Robust Estimators: Beyond Series Regression
Then, assuming the nuisance functions are H\"{o}lder smooth, but without assuming knowledge of the true smoothness level or the covariate density, we establish that DCDR estimators with several linear smoothers are semiparametric efficient under minimal conditions and achieve fast convergence rates in the non-$\sqrt{n}$ regime.
Continuous Treatment Effects with Surrogate Outcomes
In this paper, we study the role of surrogates in estimating continuous treatment effects and propose a doubly robust method to efficiently incorporate surrogates in the analysis, which uses both labeled and unlabeled data and does not suffer from the above selection bias problem.
The Fundamental Limits of Structure-Agnostic Functional Estimation
These first-order methods are however provably suboptimal in a minimax sense for functional estimation when the nuisance functions live in Holder-type function spaces.
Doubly Robust Counterfactual Classification
We study counterfactual classification as a new tool for decision-making under hypothetical (contrary to fact) scenarios.
The role of the geometric mean in case-control studies
We provide a new definition of collapsibility that makes this choice of aggregation method explicit, and we demonstrate that the odds ratio is collapsible under geometric aggregation.
Time-uniform central limit theory and asymptotic confidence sequences
This paper introduces time-uniform analogues of such asymptotic confidence intervals, adding to the literature on confidence sequences (CS) -- sequences of confidence intervals that are uniformly valid over time -- which provide valid inference at arbitrary stopping times and incur no penalties for "peeking" at the data, unlike classical confidence intervals which require the sample size to be fixed in advance.
Median Optimal Treatment Regimes
In this work, we propose a new median optimal treatment regime that instead treats individuals whose conditional median is higher under treatment.
Counterfactual Predictions under Runtime Confounding
We propose a doubly-robust procedure for learning counterfactual prediction models in this setting.
Counterfactual Risk Assessments, Evaluation, and Fairness
These tools thus reflect risk under the historical policy, rather than under the different decision options that the tool is intended to inform.
Incremental Intervention Effects in Studies with Dropout and Many Timepoints
Modern longitudinal studies collect feature data at many timepoints, often of the same order of sample size.
Causal effects based on distributional distances
In this paper we develop a framework for characterizing causal effects via distributional distances.
Nonparametric Double Robustness
We use 10, 000 simulated samples and 50, 100, 200, 600, and 1200 observations to investigate the bias and mean squared error of singly robust (g Computation, inverse probability weighting) and doubly robust (augmented inverse probability weighting, targeted maximum likelihood estimation) estimators under four scenarios: correct and incorrect model specification; and parametric and nonparametric estimation.
Methodology
