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学习最优分布鲁棒个体化治疗规则AI 解读

Recent development in the data-driven decision science has seen great advances in individualized decision making. Given data with individual covariates, treatment assignments and outcomes, policy makers best individualized treatment rule (ITR) that maximizes the expected outcome, known as the value function. Many existing methods assume that the training and testing distributions are the same. However, the estimated optimal ITR may have poor generalizability when the…

同伴效应观察性研究中的偏差与高维调整AI 解读

Peer effects, in which an individual’s behavior is affected by peers’ behavior, are posited by multiple theories in the social sciences. Randomized field experiments that identify peer effects, however, are often expensive or infeasible, so many studies of peer effects use observational data, which is expected to suffer from confounding. Here we show, in the context of information and media diffusion, that high-dimensional adjustment of a nonexperimental control group (660…

一类新的样本平均处理效应的统计推断AI 解读

We derive new variance formulas for inference on a general class of estimands of causal average treatment effects in a randomized control trial. We generalize the seminal work of Robins and show that when the researcher’s objective is inference on sample average treatment effect of the treated (SATT), a consistent variance estimator exists. Although this estimand is equal to the sample average treatment effect (SATE) in expectation, potentially large differences in both…

通过最优目标人群重设提高策略学习效率AI 解读

Nathan Kallus* School of Operations Research and Information Engineering and Cornell Tech, Cornell University, New York, NYColor versions of one or more of the figures in the article can be found online at www.tandfonline.com/r/JASA.Supplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.CONTACT Nathan Kallus kallus@cornell.edu School of Operations Research and Information Engineering and Cornell Tech, Cornell University, 2 West…

两阶段随机实验中存在干扰与不依从时的因果推断AI 解读

In many social science experiments, subjects often interact with each other and as a result one unit’s treatment influences the outcome of another unit. Over the last decade, a significant progress has been made toward causal inference in the presence of such interference between units. Researchers have shown that the two-stage randomization of treatment assignment enables the identification of average direct and spillover effects. However, much of the literature has assumed…

多层基因组网络中的贝叶斯结构学习AI 解读

Integrative network modeling of data arising from multiple genomic platforms provides insight into the holistic picture of the interactive system, as well as the flow of information across many disease domains including cancer. The basic data structure consists of a sequence of hierarchically ordered datasets for each individual subject, which facilitates integration of diverse inputs, such as genomic, transcriptomic, and proteomic data. A primary analytical task in such…

以无向网络为预测变量的贝叶斯回归：脑连接组应用AI 解读

This article focuses on the relationship between a measure of creativity and the human brain network for subjects in a brain connectome dataset obtained using a diffusion weighted magnetic resonance imaging procedure. We identify brain regions and interconnections that have a significant effect on creativity. Brain networks are often expressed in terms of symmetric adjacency matrices, with row and column indices of the matrix representing the regions of interest (ROI), and a…

网络观察性研究中处理效应与干扰效应的识别和估计AI 解读

Laura Forastierea , Edoardo M. Airoldib* & Fabrizia Meallica Department of Biostatistics, Yale University, New Haven, CTb Department of Statistical Science, Fox School of Business, Temple University, Philadelphia, PAc Department of Statistics, Informatics, Applications “G. Parenti”, University of Florence, Florence, ItalySupplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.CONTACT Edoardo M. Airoldi airoldi@temple.edu…

稀疏图中协变量正则化的社区发现AI 解读

In this article, we investigate community detection in networks in the presence of node covariates. In many instances, covariates and networks individually only give a partial view of the cluster structure. One needs to jointly infer the full cluster structure by considering both. In statistics, an emerging body of work has been focused on combining information from both the edges in the network and the node covariates to infer community memberships. However, so far the…

感染图的置换检验AI 解读

We formulate and analyze a novel hypothesis testing problem for inferring the edge structure of an infection graph. In our model, a disease spreads over a network via contagion or random infection, where the times between successive contagion events are independent exponential random variables with unknown rate parameters. A subset of nodes is also censored uniformly at random. Given the observed infection statuses of nodes in the network, the goal is to determine the…

删失数据的变换动态分位数回归AI 解读

We propose a class of power-transformed linear quantile regression models for time-to-event observations subject to censoring. By introducing a process of power transformation with different transformation parameters at individual quantile levels, our framework relaxes the assumption of logarithmic transformation on survival times and provides dynamic estimation of various quantile levels. With such formulation, our proposal no longer requires the potentially restrictive…

基于 Copula 的聚类数据随机效应模型AI 解读

In a binary choice panel data framework, probabilities of the outcomes of several individuals depend on the correlation of the unobserved heterogeneity. I propose a random effects estimator that models the correlation of the unobserved heterogeneity among individuals in the same cluster using a copula. I discuss the asymptotic efficiency of the estimator relative to standard random effects estimators, and to choose the copula I propose a specification test. The…

兼顾平等的处理分配决策AI 解读

The goal of many randomized experiments and quasi-experimental studies in economics is to inform policies that aim to raise incomes and reduce economic inequality. A policy maximizing the sum of individual incomes may not be desirable if it magnifies economic inequality and post-treatment redistribution of income is infeasible. This article develops a method to estimate the optimal treatment assignment policy based on observable individual covariates when the policy…

重新考察食品价格对冲突的影响AI 解读

Studies that examine the impact of food prices on conflict usually assume that (all) changes in international food prices are exogenous shocks for individual countries or local areas. By isolating strictly exogenous shifts in global food commodity prices, we show that this assumption could seriously distort estimations of the impact on conflict in African regions. Specifically, we show that increases in food prices that are caused by harvest shocks outside Africa raise…

处理效应分析中联合分布泛函的尖锐界AI 解读

This article proposes an identification and estimation method that allows researchers to bound continuous functionals of the joint distribution of potential outcomes from the literature on treatment effects. The focus is on a model where no restrictions are imposed on treatment selection. The method can sharply bound interesting parameters when analytical bounds are difficult to derive, can be used in settings in which instruments are available, and can easily accommodate…

区分个体处理效应与溢出效应的框架AI 解读

This article suggests a causal framework for separating individual-level treatment effects and spillover effects such as general equilibrium, interference, or interaction effects related to treatment distribution. We relax the stable unit treatment value assumption assuming away treatment-dependent interaction between study participants and permit spillover effects within aggregates, for example, regions. Based on our framework, we systematically categorize the…

不依赖偏好相似性限制的集体家庭随机资源份额识别AI 解读

Resource shares, defined as the fraction of total household spending going to each person in a household, are important for assessing individual material well-being, inequality, and poverty. They are difficult to identify because consumption is measured typically at the household level, and many goods are jointly consumed, so that individual level consumption in multi-person households is not directly observed. We consider random resource shares, which vary across…

第一价格拍卖模型的半参数估计AI 解读

In this article, we propose a two-step semiparametric procedure to estimate first-price auction models. In the first step, we estimate the bid density and distribution using local polynomial method, and recover a sample of (pseudo) private values. In the second step, we apply the method of moments to the sample of private values to estimate a finite set of parameters that characterize the density of private values. We show that our estimator attains the parametric…

面板数据单指标模型中的同质结构识别AI 解读

Panel data analysis is an important topic in statistics and econometrics. Traditionally, in panel data analysis, all individuals are assumed to share the same unknown parameters, e.g. the same coefficients of covariates when the linear models are used, and the differences between the individuals are accounted for by cluster effects. This kind of modelling only makes sense if our main interest is on the global trend, this is because it would not be able to tell us anything…

上游与下游：通用产品代码的扩散及其影响AI 解读

We study the adoption, diffusion, and impacts of the Universal Product Code (UPC) between 1975 and 1992, during the initial years of the bar code system. We find evidence of network effects in the diffusion process. Matched-sample difference-in-differences estimates show that firm size and trademark registrations increase following UPC adoption by manufacturers. Industry-level import penetration also increases with domestic UPC adoption. Our findings suggest that bar codes,…

实验设计中随机化的最优性：极小极大方差与基于设计的推断AI 解读

Abstract I study the minimax-optimal design for a two-arm controlled experiment where conditional mean outcomes vary in a given set and the objective is effect-estimation precision. When this set is permutation symmetric, the optimal design is shown to be complete randomization. Notably, even when the set has structure (i.e., is not permutation symmetric), being minimax-optimal for precision still requires randomization beyond a single partition of units, that is, beyond…

利用工具变量估计最优治疗规则：部分识别学习方法AI 解读

Abstract Individualized treatment rules (ITRs) are considered a promising recipe to deliver better policy interventions. One key ingredient in optimal ITR estimation problems is to estimate the average treatment effect conditional on a subject’s covariate information, which is often challenging in observational studies due to the universal concern of unmeasured confounding. Instrumental variables (IVs) are widely used tools to infer the treatment effect when there is…

锚回归：异质数据与因果性AI 解读

Abstract We consider the problem of predicting a response variable from a set of covariates on a data set that differs in distribution from the training data. Causal parameters are optimal in terms of predictive accuracy if in the new distribution either many variables are affected by interventions or only some variables are affected, but the perturbations are strong. If the training and test distributions differ by a shift, causal parameters might be too conservative to…

边学边做（LAGO）自适应研究的分析AI 解读

In Learn-As-you-GO (LAGO) adaptive studies, the intervention is a complex multicomponent package, and is adapted in stages during the study based on past outcome data. This design formalizes standard practice in public health intervention studies. An effective intervention package is sought, while minimizing intervention package cost. In LAGO study data, the interventions in later stages depend upon the outcomes in the previous stages, violating standard statistical theory.…