Journal of Causal Inference最新文献_第5页

Double machine learning and automated confounder selection: A cautionary tale 双重机器学习和自动混淆选择:一个警世故事

IF 1.4 4区医学

Journal of Causal Inference Pub Date : 2021-08-25 DOI: 10.1515/jci-2022-0078

Paul Hünermund, Beyers Louw, Itamar Caspi

引用次数: 7

Adaptive normalization for IPW estimation IPW估计的自适应归一化

IF 1.4 4区医学

Journal of Causal Inference Pub Date : 2021-06-14 DOI: 10.1515/jci-2022-0019

Samir Khan, J. Ugander

{"title":"Adaptive normalization for IPW estimation","authors":"Samir Khan, J. Ugander","doi":"10.1515/jci-2022-0019","DOIUrl":"https://doi.org/10.1515/jci-2022-0019","url":null,"abstract":"Abstract Inverse probability weighting (IPW) is a general tool in survey sampling and causal inference, used in both Horvitz–Thompson estimators, which normalize by the sample size, and Hájek/self-normalized estimators, which normalize by the sum of the inverse probability weights. In this work, we study a family of IPW estimators, first proposed by Trotter and Tukey in the context of Monte Carlo problems, that are normalized by an affine combination of the sample size and a sum of inverse weights. We show how selecting an estimator from this family in a data-dependent way to minimize asymptotic variance leads to an iterative procedure that converges to an estimator with connections to regression control methods. We refer to such estimators as adaptively normalized estimators. For mean estimation in survey sampling, the adaptively normalized estimator has asymptotic variance that is never worse than the Horvitz–Thompson and Hájek estimators. Going further, we show that adaptive normalization can be used to propose improvements of the augmented IPW (AIPW) estimator, average treatment effect (ATE) estimators, and policy learning objectives. Appealingly, these proposals preserve both the asymptotic efficiency of AIPW and the regret bounds for policy learning with IPW objectives, and deliver consistent finite sample improvements in simulations for all three of mean estimation, ATE estimation, and policy learning.","PeriodicalId":48576,"journal":{"name":"Journal of Causal Inference","volume":"49 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83878902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Causal effect on a target population: A sensitivity analysis to handle missing covariates 对目标人群的因果效应:处理缺失协变量的敏感性分析

IF 1.4 4区医学

Journal of Causal Inference Pub Date : 2021-05-13 DOI: 10.1515/jci-2021-0059

B. Colnet, J. Josse, G. Varoquaux, Erwan Scornet

{"title":"Causal effect on a target population: A sensitivity analysis to handle missing covariates","authors":"B. Colnet, J. Josse, G. Varoquaux, Erwan Scornet","doi":"10.1515/jci-2021-0059","DOIUrl":"https://doi.org/10.1515/jci-2021-0059","url":null,"abstract":"Abstract Randomized controlled trials (RCTs) are often considered the gold standard for estimating causal effect, but they may lack external validity when the population eligible to the RCT is substantially different from the target population. Having at hand a sample of the target population of interest allows us to generalize the causal effect. Identifying the treatment effect in the target population requires covariates to capture all treatment effect modifiers that are shifted between the two sets. Standard estimators then use either weighting (IPSW), outcome modeling (G-formula), or combine the two in doubly robust approaches (AIPSW). However, such covariates are often not available in both sets. In this article, after proving L 1 {L}^{1} -consistency of these three estimators, we compute the expected bias induced by a missing covariate, assuming a Gaussian distribution, a continuous outcome, and a semi-parametric model. Under this setting, we perform a sensitivity analysis for each missing covariate pattern and compute the sign of the expected bias. We also show that there is no gain in linearly imputing a partially unobserved covariate. Finally, we study the substitution of a missing covariate by a proxy. We illustrate all these results on simulations, as well as semi-synthetic benchmarks using data from the Tennessee student/teacher achievement ratio (STAR), and a real-world example from critical care medicine.","PeriodicalId":48576,"journal":{"name":"Journal of Causal Inference","volume":"19 1","pages":"372 - 414"},"PeriodicalIF":1.4,"publicationDate":"2021-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88798547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Precise unbiased estimation in randomized experiments using auxiliary observational data 利用辅助观测数据进行随机实验的精确无偏估计

IF 1.4 4区医学

Journal of Causal Inference Pub Date : 2021-05-07 DOI: 10.1515/jci-2022-0011

Johann A. Gagnon-Bartsch, Adam C. Sales, Edward Wu, Anthony F. Botelho, John A. Erickson, Luke W. Miratrix, N. Heffernan

{"title":"Precise unbiased estimation in randomized experiments using auxiliary observational data","authors":"Johann A. Gagnon-Bartsch, Adam C. Sales, Edward Wu, Anthony F. Botelho, John A. Erickson, Luke W. Miratrix, N. Heffernan","doi":"10.1515/jci-2022-0011","DOIUrl":"https://doi.org/10.1515/jci-2022-0011","url":null,"abstract":"Abstract Randomized controlled trials (RCTs) admit unconfounded design-based inference – randomization largely justifies the assumptions underlying statistical effect estimates – but often have limited sample sizes. However, researchers may have access to big observational data on covariates and outcomes from RCT nonparticipants. For example, data from A/B tests conducted within an educational technology platform exist alongside historical observational data drawn from student logs. We outline a design-based approach to using such observational data for variance reduction in RCTs. First, we use the observational data to train a machine learning algorithm predicting potential outcomes using covariates and then use that algorithm to generate predictions for RCT participants. Then, we use those predictions, perhaps alongside other covariates, to adjust causal effect estimates with a flexible, design-based covariate-adjustment routine. In this way, there is no danger of biases from the observational data leaking into the experimental estimates, which are guaranteed to be exactly unbiased regardless of whether the machine learning models are “correct” in any sense or whether the observational samples closely resemble RCT samples. We demonstrate the method in analyzing 33 randomized A/B tests and show that it decreases standard errors relative to other estimators, sometimes substantially.","PeriodicalId":48576,"journal":{"name":"Journal of Causal Inference","volume":"146 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2021-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86836047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Simple yet sharp sensitivity analysis for unmeasured confounding 对未测量混杂的简单而敏锐的敏感性分析

IF 1.4 4区医学

Journal of Causal Inference Pub Date : 2021-04-27 DOI: 10.1515/jci-2021-0041

J. Peña

引用次数: 4

Causality and independence in perfectly adapted dynamical systems 完全适应动力系统中的因果关系和独立性

IF 1.4 4区医学

Journal of Causal Inference Pub Date : 2021-01-28 DOI: 10.1515/jci-2021-0005

Tineke Blom, J. Mooij

引用次数: 7

Parental Methamphetamine Abuse and Foster Care 父母滥用甲基苯丙胺及寄养