Observational studies最新文献_第5页

Sensitivity Analysis for the Adjusted Mann-Whitney Test with Observational Studies 观察性研究校正Mann-Whitney检验的敏感性分析

Observational studies Pub Date : 2022-06-04 DOI: 10.1353/obs.2022.0002

Maozhu Dai, Weining Shen, H. Stern

引用次数: 1

Evaluation of Language Training Programs in Luxembourg using Principal Stratification 用校长分层法评价卢森堡语言培训项目

Observational studies Pub Date : 2022-06-04 DOI: 10.2139/ssrn.3538309

Michela Bia, Alfonso Flores-Lagunes, Andrea Mercatanti

{"title":"Evaluation of Language Training Programs in Luxembourg using Principal Stratification","authors":"Michela Bia, Alfonso Flores-Lagunes, Andrea Mercatanti","doi":"10.2139/ssrn.3538309","DOIUrl":"https://doi.org/10.2139/ssrn.3538309","url":null,"abstract":"Abstract:In a world increasingly globalized, multiple language skills can create more employment opportunities. Several countries include language training programs in active labor market programs for the unemployed. We analyze the effects of a language training program on the re-employment probability and hourly wages simultaneously, using high-quality administrative data from Luxembourg. We address selection into training with an unconfoundedness assumption and account for the complication that wages are “truncated” by unemployment by adopting a principal stratification framework. Estimation is undertaken with a mixture model likelihood-based approach. To improve inference, we use the individual’s hours worked as a secondary outcome and a stochastic dominance assumption. These two features considerably ameliorate the multimodality problem commonly encountered in mixture models. We also conduct a sensitivity analysis to assess the unconfoundedness assumption. Our results suggest a positive effect (of up to 12.7 percent) of the language training programs on the re-employment probability, but no effects on wages for those who are observed employed regardless of training participation.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"8 1","pages":"1 - 44"},"PeriodicalIF":0.0,"publicationDate":"2022-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42577592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

gesttools: General Purpose G-Estimation in R gestools:R中的通用G-估计

Observational studies Pub Date : 2022-06-04 DOI: 10.1353/obs.2022.0003

Daniel Tompsett, S. Vansteelandt, O. Dukes, B. D. De Stavola

引用次数: 1

Causal Inference Challenges with Interrupted Time Series Designs: An Evaluation of an Assault Weapons Ban in California 中断时间序列设计的因果推理挑战:对加州攻击性武器禁令的评估

Observational studies Pub Date : 2022-04-26 DOI: 10.1353/obs.0.0001

R. Berk

引用次数: 1

Posterior Predictive Propensity Scores and p-Values 后验预测倾向评分和p值

Observational studies Pub Date : 2022-02-16 DOI: 10.1353/obs.2023.0015

Peng Ding, Tianyu Guo

{"title":"Posterior Predictive Propensity Scores and p-Values","authors":"Peng Ding, Tianyu Guo","doi":"10.1353/obs.2023.0015","DOIUrl":"https://doi.org/10.1353/obs.2023.0015","url":null,"abstract":"Abstract:Rosenbaum and Rubin (1983) introduced the notion of the propensity score and discussed its central role in causal inference with observational studies. Their paper, however, caused a fundamental incoherence with an early paper by Rubin (1978), which showed that the propensity score does not play any role in the Bayesian analysis of unconfounded observational studies if the priors on the propensity score and outcome models are independent. Despite the serious efforts made in the literature, it is generally difficult to reconcile these contradicting results. We offer a simple approach to incorporating the propensity score in Bayesian causal inference based on the posterior predictive p-value. To motivate a simple procedure, we focus on the model with the strong null hypothesis of no causal effects for any units whatsoever. Computationally, the proposed posterior predictive p-value equals the classic p-value based on the Fisher randomization test averaged over the posterior predictive distribution of the propensity score. Moreover, using the studentized doubly robust estimator as the test statistic, the proposed p-value inherits the doubly robust property and is also asymptotically valid for testing the weak null hypothesis of zero average causal effect. Perhaps surprisingly, this Bayesianly motivated p-value can have better frequentist’s finite-sample performance than the frequentist’s p-value based on the asymptotic approximation especially when the propensity scores can take extreme values.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"9 1","pages":"18 - 3"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49379109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Melting together prediction and inference 将预测和推理融合在一起

Observational studies Pub Date : 2021-10-04 DOI: 10.1353/obs.2021.0035

A. Daoud, Devdatt P. Dubhashi

{"title":"Melting together prediction and inference","authors":"A. Daoud, Devdatt P. Dubhashi","doi":"10.1353/obs.2021.0035","DOIUrl":"https://doi.org/10.1353/obs.2021.0035","url":null,"abstract":"Abstract:In Leo Breiman's influential article \"Statistical modeling-the two cultures\" he identified two cultures for statistical practices. The data modeling culture (DMC) denotes practices tailored for statistical inference targeting a quantity of interest, [inline-graphic 01]. The algorithmic modeling culture (AMC) refers to practices defining an algorithm, or a machine-learning (ML) procedure, that generates accurate predictions about an outcome of interest, [inline-graphic 02] was the dominant mode, Breiman argued that statisticians should give more attention to AMC. Twenty years later and energized by two revolutions—one in data-science and one in causal inference—a hybrid modeling culture (HMC) is rising. HMC fuses the inferential strength of DMC and the predictive power of AMC with the goal of analyzing cause and effect, and thus, HMC's quantity of interest is causal effect, [inline-graphic 03]. In combining inference and prediction, the result of HMC practices is that the distinction between prediction and inference, taken to its limit, melts away. While this hybrid culture does not occupy the default mode of scientific practices, we argue that it offers an intriguing novel path for applied sciences.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"7 1","pages":"1 - 7"},"PeriodicalIF":0.0,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45501762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Randomization Tests to Assess Covariate Balance When Designing and Analyzing Matched Datasets 设计和分析匹配数据集时评估协变量平衡的随机测试

Observational studies Pub Date : 2021-09-09 DOI: 10.1353/obs.2021.0031

Zach Branson

{"title":"Randomization Tests to Assess Covariate Balance When Designing and Analyzing Matched Datasets","authors":"Zach Branson","doi":"10.1353/obs.2021.0031","DOIUrl":"https://doi.org/10.1353/obs.2021.0031","url":null,"abstract":"Abstract:Causal analyses for observational studies are often complicated by covariate imbalances among treatment groups, and matching methodologies alleviate this complication by finding subsets of treatment groups that exhibit covariate balance. It is widely agreed upon that covariate balance can serve as evidence that a matched dataset approximates a randomized experiment, but what kind of experiment does a matched dataset approximate? In this work, we develop a randomization test for the hypothesis that a matched dataset approximates a particular experimental design, such as complete randomization, block randomization, or rerandomization. Our test can incorporate any experimental design, and it allows for a graphical display that puts several designs on the same univariate scale, thereby allowing researchers to pinpoint which design—if any—is most appropriate for a matched dataset. After researchers determine a plausible design, we recommend a randomization based approach for analyzing the matched data, which can incorporate any design and treatment effect estimator. Through simulation, we find that our test can frequently detect violations of randomized assignment that harm inferential results. Furthermore, through simulation and a real application in political science, we find that matched datasets with high levels of covariate balance tend to approximate balance-constrained designs like rerandomization, and analyzing them as such can lead to precise causal analyses. However, assuming a precise design should be proceeded with caution, because it can harm inferential results if there are still substantial biases due to remaining imbalances after matching. Our approach is implemented in the randChecks R package, available on CRAN.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"7 1","pages":"1 - 36"},"PeriodicalIF":0.0,"publicationDate":"2021-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44904534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Protocol: Evaluating the Effect of ACA Medicaid Expansion on 2015-2018 Mortality Through Matching and Weighting 方案：通过匹配和加权评估ACA医疗补助扩展对2015-2018年死亡率的影响

Observational studies Pub Date : 2021-09-09 DOI: 10.1353/obs.2021.0033

Timothy Lycurgus, Charlotte Z. Mann, B. Hansen

引用次数: 0

Breiman's Two Cultures: A Perspective from Econometrics 布雷曼的两种文化:计量经济学的视角

Observational studies Pub Date : 2021-07-27 DOI: 10.1353/obs.2021.0028

G. Imbens, S. Athey

引用次数: 6

Reflections on Breiman's Two Cultures of Statistical Modeling 对Breiman的两种统计建模文化的思考

Observational studies Pub Date : 2021-07-27 DOI: 10.1353/obs.2021.0025

A. Gelman

引用次数: 2