Biometrika

最新文章

• A simple and general debiased machine learning theorem with finite-sample guarantees AI翻译
  作者：Chernozhukov, V; Newey, W. K.; Singh, R.
  作者单位：Massachusetts Institute of Technology (MIT)
  摘要：Debiased machine learning is a meta-algorithm based on bias correction and sample splitting to calculate confidence intervals for functionals, i.e., scalar summaries, of machine learning algorithms. For example, an analyst may seek the confidence interval for a treatment effect estimated with a neural network. We present a non-asymptotic debiased machine learning theorem that encompasses any global or local functional of any machine learning algorithm that satisfies a few simple, interpretable...
• Soft calibration for selection bias problems under mixed-effects models AI翻译
  作者：Gao, Chenyin; Yang, Shu; Kim, Jae Kwang
  作者单位：North Carolina State University; Iowa State University
  摘要：Calibration weighting has been widely used to correct selection biases in nonprobability sampling, missing data and causal inference. The main idea is to calibrate the biased sample to the benchmark by adjusting the subject weights. However, hard calibration can produce enormous weights when an exact calibration is enforced on a large set of extraneous covariates. This article proposes a soft calibration scheme, where the outcome and the selection indicator follow mixed-effect models. The sche...
• Efficient estimation under data fusion AI翻译
  作者：Li, Sijia; Luedtke, Alex
  作者单位：University of Washington; University of Washington Seattle
  摘要：We aim to make inferences about a smooth, finite-dimensional parameter by fusing together data from multiple sources. Previous works have studied the estimation of a variety of parameters in similar data fusion settings, including estimation of the average treatment effect and average reward under a policy, with the majority of them merging one historical data source with covariates, actions and rewards, and one data source of the same covariates. In this article, we consider the general case ...
• Semiparametric counterfactual density estimation AI翻译
  作者：Kennedy, E. H.; Balakrishnan, S.; Wasserman, L. A.
  作者单位：Carnegie Mellon University
  摘要：Causal effects are often characterized with averages, which can give an incomplete picture of the underlying counterfactual distributions. Here we consider estimating the entire counterfactual density and generic functionals thereof. We focus on two kinds of target parameters: density approximations and the distance between counterfactual densities. We study nonparametric efficiency bounds, giving results for smooth but otherwise generic models and distances. Importantly, we show how these bou...
• Proximal mediation analysis AI翻译
  作者：Dukes, Oliver; Shpitser, Ilya; Tchetgen, Eric J. Tchetgen
  作者单位：Ghent University; Johns Hopkins University; University of Pennsylvania
  摘要：A common concern when trying to draw causal inferences from observational data is that the measured covariates are insufficiently rich to account for all sources of confounding. In practice, many of the covariates may only be proxies of the latent confounding mechanism. Recent work has shown that in certain settings where the standard no-unmeasured-confounding assumption fails, proxy variables can be leveraged to identify causal effects. Results currently exist for the total causal effect of a...
• Order-based structure learning without score equivalence AI翻译
  作者：Chang, Hyunwoong; Cai, James J.; Zhou, Quan
  作者单位：Texas A&M University System; Texas A&M University College Station; Texas A&M University System; Texas A&M University College Station
  摘要：We propose an empirical Bayes formulation of the structure learning problem, where the prior specification assumes that all node variables have the same error variance, an assumption known to ensure the identifiability of the underlying causal directed acyclic graph. To facilitate efficient posterior computation, we approximate the posterior probability of each ordering by that of a best directed acyclic graph model, which naturally leads to an order-based Markov chain Monte Carlo algorithm. S...
• Discussion of 'Statistical inference for streamed longitudinal data' AI翻译
  作者：Ning, Yang; Duan, Jingyi
  作者单位：Cornell University
• Ancestor regression in linear structural equation models (Mar, asad008, 2023) AI翻译
  作者：Schultheiss, C.; Buhlmann, P.
• Clustering consistency with Dirichlet process mixtures AI翻译
  作者：Ascolani, F.; Lijoi, A.; Rebaudo, G.; Zanella, G.
  作者单位：Bocconi University; Bocconi University; University of Texas System; University of Texas Austin; Bocconi University; Bocconi University
  摘要：Dirichlet process mixtures are flexible nonparametric models, particularly suited to density estimation and probabilistic clustering. In this work we study the posterior distribution induced by Dirichlet process mixtures as the sample size increases, and more specifically focus on consistency for the unknown number of clusters when the observed data are generated from a finite mixture. Crucially, we consider the situation where a prior is placed on the concentration parameter of the underlying...
• Optimal post-selection inference for sparse signals: a nonparametric empirical Bayes approach AI翻译
  作者：Woody, S.; Padilla, O. H. M.; Scott, J. G.
  作者单位：University of Texas System; University of Texas Austin; University of California System; University of California Los Angeles; University of Texas System; University of Texas Austin
  摘要：Many recently developed Bayesian methods focus on sparse signal detection. However, much less work has been done on the natural follow-up question: how does one make valid inferences for the magnitude of those signals after selection? Ordinary Bayesian credible intervals suffer from selection bias, as do ordinary frequentist confidence intervals. Existing Bayesian methods for correcting this bias produce credible intervals with poor frequentist properties. Further, existing frequentist approac...