Journal of Causal Inference最新文献_第10页

Novel bounds for causal effects based on sensitivity parameters on the risk difference scale 基于风险差异量表上敏感性参数的因果效应的新界限

IF 1.4 4区医学

Journal of Causal Inference Pub Date : 2021-01-01 DOI: 10.1515/jci-2021-0024

A. Sjölander, O. Hössjer

引用次数: 4

Instrumental variable regression via kernel maximum moment loss 通过核最大矩损失的工具变量回归

IF 1.4 4区医学

Journal of Causal Inference Pub Date : 2020-10-15 DOI: 10.1515/jci-2022-0073

Rui Zhang, M. Imaizumi, B. Scholkopf, Krikamol Muandet

引用次数: 1

Learning linear non-Gaussian graphical models with multidirected edges 学习具有多向边的线性非高斯图形模型

IF 1.4 4区医学

Journal of Causal Inference Pub Date : 2020-10-11 DOI: 10.1515/jci-2020-0027

Yiheng Liu, Elina Robeva, Huanqing Wang

引用次数: 0

Randomized graph cluster randomization 随机图聚类随机化

IF 1.4 4区医学

Journal of Causal Inference Pub Date : 2020-09-04 DOI: 10.1515/jci-2022-0014

J. Ugander, Hao Yin

{"title":"Randomized graph cluster randomization","authors":"J. Ugander, Hao Yin","doi":"10.1515/jci-2022-0014","DOIUrl":"https://doi.org/10.1515/jci-2022-0014","url":null,"abstract":"Abstract The global average treatment effect (GATE) is a primary quantity of interest in the study of causal inference under network interference. With a correctly specified exposure model of the interference, the Horvitz–Thompson (HT) and Hájek estimators of the GATE are unbiased and consistent, respectively, yet known to exhibit extreme variance under many designs and in many settings of interest. With a fixed clustering of the interference graph, graph cluster randomization (GCR) designs have been shown to greatly reduce variance compared to node-level random assignment, but even so the variance is still often prohibitively large. In this work, we propose a randomized version of the GCR design, descriptively named randomized graph cluster randomization (RGCR), which uses a random clustering rather than a single fixed clustering. By considering an ensemble of many different clustering assignments, this design avoids a key problem with GCR where the network exposure probability of a given node can be exponentially small in a single clustering. We propose two inherently randomized graph decomposition algorithms for use with RGCR designs, randomized 3-net and 1-hop-max, adapted from the prior work on multiway graph cut problems and the probabilistic approximation of (graph) metrics. We also propose weighted extensions of these two algorithms with slight additional advantages. All these algorithms result in network exposure probabilities that can be estimated efficiently. We derive structure-dependent upper bounds on the variance of the HT estimator of the GATE, depending on the metric structure of the graph driving the interference. Where the best-known such upper bound for the HT estimator under a GCR design is exponential in the parameters of the metric structure, we give a comparable upper bound under RGCR that is instead polynomial in the same parameters. We provide extensive simulations comparing RGCR and GCR designs, observing substantial improvements in GATE estimation in a variety of settings.","PeriodicalId":48576,"journal":{"name":"Journal of Causal Inference","volume":"77 2-3 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2020-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78140869","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: 20

Estimating causal effects with the neural autoregressive density estimator 用神经自回归密度估计器估计因果效应

IF 1.4 4区医学

Journal of Causal Inference Pub Date : 2020-08-17 DOI: 10.1515/jci-2020-0007

Sergio Garrido, S. Borysov, Jeppe Rich, F. Pereira

引用次数: 4

Conditional as-if analyses in randomized experiments 随机实验中的条件假设分析

IF 1.4 4区医学

Journal of Causal Inference Pub Date : 2020-08-03 DOI: 10.1515/jci-2021-0012

Nicole E. Pashley, Guillaume W. Basse, Luke W. Miratrix

{"title":"Conditional as-if analyses in randomized experiments","authors":"Nicole E. Pashley, Guillaume W. Basse, Luke W. Miratrix","doi":"10.1515/jci-2021-0012","DOIUrl":"https://doi.org/10.1515/jci-2021-0012","url":null,"abstract":"Abstract The injunction to “analyze the way you randomize” is well known to statisticians since Fisher advocated for randomization as the basis of inference. Yet even those convinced by the merits of randomization-based inference seldom follow this injunction to the letter. Bernoulli randomized experiments are often analyzed as completely randomized experiments, and completely randomized experiments are analyzed as if they had been stratified; more generally, it is not uncommon to analyze an experiment as if it had been randomized differently. This article examines the theoretical foundation behind this practice within a randomization-based framework. Specifically, we ask when is it legitimate to analyze an experiment randomized according to one design as if it had been randomized according to some other design. We show that a sufficient condition for this type of analysis to be valid is that the design used for analysis should be derived from the original design by an appropriate form of conditioning. We use our theory to justify certain existing methods, question others, and finally suggest new methodological insights such as conditioning on approximate covariate balance.","PeriodicalId":48576,"journal":{"name":"Journal of Causal Inference","volume":"92 1","pages":"264 - 284"},"PeriodicalIF":1.4,"publicationDate":"2020-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83798256","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

Properties of restricted randomization with implications for experimental design 限制随机化的性质及其对实验设计的影响

IF 1.4 4区医学

Journal of Causal Inference Pub Date : 2020-06-26 DOI: 10.1515/jci-2021-0057

Mattias Nordin, M. Schultzberg

{"title":"Properties of restricted randomization with implications for experimental design","authors":"Mattias Nordin, M. Schultzberg","doi":"10.1515/jci-2021-0057","DOIUrl":"https://doi.org/10.1515/jci-2021-0057","url":null,"abstract":"Abstract Recently, there has been increasing interest in the use of heavily restricted randomization designs which enforce balance on observed covariates in randomized controlled trials. However, when restrictions are strict, there is a risk that the treatment effect estimator will have a very high mean squared error (MSE). In this article, we formalize this risk and propose a novel combinatoric-based approach to describe and address this issue. First, we validate our new approach by re-proving some known properties of complete randomization and restricted randomization. Second, we propose a novel diagnostic measure for restricted designs that only use the information embedded in the combinatorics of the design. Third, we show that the variance of the MSE of the difference-in-means estimator in a randomized experiment is a linear function of this diagnostic measure. Finally, we identify situations in which restricted designs can lead to an increased risk of getting a high MSE and discuss how our diagnostic measure can be used to detect such designs. Our results have implications for any restricted randomization design and can be used to evaluate the trade-off between enforcing balance on observed covariates and avoiding too restrictive designs.","PeriodicalId":48576,"journal":{"name":"Journal of Causal Inference","volume":"39 1","pages":"227 - 245"},"PeriodicalIF":1.4,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89531588","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: 6

Decision-theoretic foundations for statistical causality 统计因果关系的决策理论基础

IF 1.4 4区医学

Journal of Causal Inference Pub Date : 2020-04-26 DOI: 10.1515/jci-2020-0008

P. Dawid

引用次数: 29

The variance of causal effect estimators for binary v-structures 二元v型结构的因果效应估计量的方差

IF 1.4 4区医学

Journal of Causal Inference Pub Date : 2020-04-20 DOI: 10.1515/jci-2021-0025

Jack Kuipers, G. Moffa

{"title":"The variance of causal effect estimators for binary v-structures","authors":"Jack Kuipers, G. Moffa","doi":"10.1515/jci-2021-0025","DOIUrl":"https://doi.org/10.1515/jci-2021-0025","url":null,"abstract":"Abstract Adjusting for covariates is a well-established method to estimate the total causal effect of an exposure variable on an outcome of interest. Depending on the causal structure of the mechanism under study, there may be different adjustment sets, equally valid from a theoretical perspective, leading to identical causal effects. However, in practice, with finite data, estimators built on different sets may display different precisions. To investigate the extent of this variability, we consider the simplest non-trivial non-linear model of a v-structure on three nodes for binary data. We explicitly compute and compare the variance of the two possible different causal estimators. Further, by going beyond leading-order asymptotics, we show that there are parameter regimes where the set with the asymptotically optimal variance does depend on the edge coefficients, a result that is not captured by the recent leading-order developments for general causal models. As a practical consequence, the adjustment set selection needs to account for the relative magnitude of the relationships between variables with respect to the sample size and cannot rely on purely graphical criteria.","PeriodicalId":48576,"journal":{"name":"Journal of Causal Inference","volume":"41 1","pages":"90 - 105"},"PeriodicalIF":1.4,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81036119","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: 2

A generalized double robust Bayesian model averaging approach to causal effect estimation with application to the study of osteoporotic fractures 基于广义双鲁棒贝叶斯模型平均法的因果效应估计及其在骨质疏松性骨折研究中的应用

IF 1.4 4区医学

Journal of Causal Inference Pub Date : 2020-03-25 DOI: 10.1515/jci-2021-0023

D. Talbot, C. Beaudoin

{"title":"A generalized double robust Bayesian model averaging approach to causal effect estimation with application to the study of osteoporotic fractures","authors":"D. Talbot, C. Beaudoin","doi":"10.1515/jci-2021-0023","DOIUrl":"https://doi.org/10.1515/jci-2021-0023","url":null,"abstract":"Abstract Analysts often use data-driven approaches to supplement their knowledge when selecting covariates for effect estimation. Multiple variable selection procedures for causal effect estimation have been devised in recent years, but additional developments are still required to adequately address the needs of analysts. We propose a generalized Bayesian causal effect estimation (GBCEE) algorithm to perform variable selection and produce double robust (DR) estimates of causal effects for binary or continuous exposures and outcomes. GBCEE employs a prior distribution that targets the selection of true confounders and predictors of the outcome for the unbiased estimation of causal effects with reduced standard errors. The Bayesian machinery allows GBCEE to directly produce inferences for its estimate. In simulations, GBCEE was observed to perform similarly or to outperform DR alternatives. Its ability to directly produce inferences is also an important advantage from a computational perspective. The method is finally illustrated for the estimation of the effect of meeting physical activity recommendations on the risk of hip or upper-leg fractures among older women in the study of osteoporotic fractures. The 95% confidence interval produced by GBCEE is 61% narrower than that of a DR estimator adjusting for all potential confounders in this illustration.","PeriodicalId":48576,"journal":{"name":"Journal of Causal Inference","volume":"3 1","pages":"335 - 371"},"PeriodicalIF":1.4,"publicationDate":"2020-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78500321","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: 3