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预算约束下组合治疗的最优个体化治疗规则AI 解读

Abstract The individualized treatment rule (ITR), which recommends an optimal treatment based on individual characteristics, has drawn considerable interest from many areas such as precision medicine, personalized education, and personalized marketing. Existing ITR estimation methods mainly adopt 1 of 2 or more treatments. However, a combination of multiple treatments could be more powerful in various areas. In this paper, we propose a novel double encoder model (DEM) to…

Abstract The aim of this article is to establish a causal link between the policies implemented by technology companies and the outcomes they yield within intricate temporal and/or spatial dependent experiments. We propose a novel temporal/spatio-temporal Varying Coefficient Decision Process model, capable of effectively capturing the evolving treatment effects in situations characterized by temporal and/or spatial dependence. Our methodology encompasses the decomposition of…

同时方向推断AI 解读

Abstract We consider the problem of inference on the signs of n>1 parameters. We aim to provide 1−α post hoc confidence bounds on the number of positive and negative (or non-positive) parameters, with a simultaneous guarantee, for all subsets of parameters. We suggest to start by using the data to select the direction of the hypothesis test for each parameter; then, adjust the p-values of the one-sided hypotheses for the selection, and use the adjusted p-values for…

因果模型的参数化与模拟AI 解读

Abstract Many statistical problems in causal inference involve a probability distribution other than the one from which data are actually observed; as an additional complication, the object of interest is often a marginal quantity of this other probability distribution. This creates many practical complications for statistical inference, even where the problem is non-parametrically identified. In particular, it is difficult to perform likelihood-based inference, or even to…

动态条件分位数处理效应的评估及网约车应用AI 解读

Many modern tech companies, such as Google, Uber, and Didi, utilize online experiments (also known as A/B testing) to evaluate new policies against existing ones. While most studies concentrate on average treatment effects, situations with skewed and heavy-tailed outcome distributions may benefit from alternative criteria, such as quantiles. However, assessing dynamic quantile treatment effects (QTE) remains a challenge, particularly when dealing with data from ride-sourcing…

高维数据的渐近分布无关独立性检验AI 解读

Test of independence is of fundamental importance in modern data analysis, with broad applications in variable selection, graphical models, and causal inference. When the data is high dimensional and the potential dependence signal is sparse, independence testing becomes very challenging without distributional or structural assumptions. In this paper, we propose a general framework for independence testing by first fitting a classifier that distinguishes the joint and…

非参数有限混合：克服误分类偏误的应用AI 解读

Investigating the differential effect of treatments in groups defined by patient characteristics is of paramount importance in personalized medicine research. In some studies, participants are first classified as having or not of the characteristic of interest by diagnostic tools, but such classifiers may not be perfectly accurate. The impact of diagnostic misclassification in statistical inference has been recently investigated in parametric model contexts and shown to…

重尾分布下极端分位数处理效应的估计与推断AI 解读

Causal inference for extreme events has many potential applications in fields such as climate science, medicine and economics. We study the extremal quantile treatment effect of a binary treatment on a continuous, heavy-tailed outcome. Existing methods are limited to the case where the quantile of interest is within the range of the observations. For applications in risk assessment, however, the most relevant cases relate to extremal quantiles that go beyond the data range.…

揭示不可观测量：多个衍生结果的因果推断AI 解读

In many applications, the interest is in treatment effects on random quantities of subjects, where those random quantities are not directly observable but can be estimated based on data from each subject. In this article, we propose a general framework for conducting causal inference in a hierarchical data generation setting. The identifiability of causal parameters of interest is shown under a condition on the biasedness of subject level estimates and an ignorability…

双重差分中构造效应界限的负相关策略AI 解读

The method of difference-in-differences (DID) is widely used to study the causal effect of policy interventions in observational studies. DID employs a before and after comparison of the treated and control units to remove bias due to time-invariant unmeasured confounders under the parallel trends assumption. Estimates from DID, however, will be biased if the outcomes for the treated and control units evolve differently in the absence of treatment, namely if the parallel…

利用潜在多中介路径消除因果推断中的混杂AI 解读

Causal effect estimation from observational data is one of the essential problems in causal inference. However, most estimation methods rely on the strong assumption that all confounders are observed, which is impractical and untestable in the real world. We develop a mediation analysis framework inferring the latent confounder for debiasing both direct and indirect causal effects. Specifically, we introduce generalized structural equation modeling that incorporates…

因子结构结果研究中对未观测混杂的敏感性分析AI 解读

In this work, we propose an approach for assessing sensitivity to unobserved confounding in studies with multiple outcomes. We demonstrate how prior knowledge unique to the multi-outcome setting can be leveraged to strengthen causal conclusions beyond what can be achieved from analyzing individual outcomes in isolation. We argue that it is often reasonable to make a shared confounding assumption, under which residual dependence amongst outcomes can be used to simplify and…

广义局部平均处理效应模型的有效稳健估计AI 解读

This article studies the estimation of causal parameters in the generalized local average treatment effect (GLATE) model, which expands upon the traditional LATE model to include multivalued treatments. We derive the efficient influence function (EIF) and the semiparametric efficiency bound (SPEB) for two types of causal parameters: the local average structural function (LASF) and the local average structural function for the treated (LASFT). The moment conditions generated…

结果依赖抽样与单调性假设下的因果推断AI 解读

We study causal inference under case-control and case-population sampling. Specifically, we focus on the binary-outcome and binary-treatment case, where the parameters of interest are causal relative and attributable risks defined via the potential outcome framework. It is shown that strong ignorability is not always as powerful as it is under random sampling and that certain monotonicity assumptions yield comparable results in terms of sharp identified intervals.…

样本选择模型的双重机器学习AI 解读

This article considers the evaluation of discretely distributed treatments when outcomes are only observed for a subpopulation due to sample selection or outcome attrition. For identification, we combine a selection-on-observables assumption for treatment assignment with either selection-on-observables or instrumental variable assumptions concerning the outcome attrition/sample selection process. We also consider dynamic confounding, meaning that covariates that jointly…

FNETS：高维时间序列的因子调整网络估计与预测AI 解读

–We propose FNETS, a methodology for network estimation and forecasting of high-dimensional time series exhibiting strong serial- and cross-sectional correlations. We operate under a factor-adjusted vector autoregressive (VAR) model which, after accounting for pervasive co-movements of the variables by common factors, models the remaining idiosyncratic dynamic dependence between the variables as a sparse VAR process. Network estimation of FNETS consists of three steps: (i)…

聚集模型中的内生性修正AI 解读

We develop a novel control function approach in models where the treatment variable has bunching at one corner of its support. This situation typically arises when the treatment variable is a constrained choice and some observations choose the corner solution. The method exploits distributional shape restrictions but makes no exclusion restrictions. We provide estimators and establish their asymptotic behavior, prove the convergence of the bootstrap, and develop tests of the…

颗粒度工具变量AI 解读

We develop a new method to construct instruments in a broad class of economic environments. In the economies we study, a few large firms, industries, or countries account for an important share of economic activity. Idiosyncratic shocks to these large players significantly affect aggregate outcomes and are valid instruments. We provide a methodology to extract these idiosyncratic shocks to create granular instrumental variables (GIVs), which are size-weighted sums of…

计量经济学因果性：思想实验的核心作用AI 解读

This paper examines the econometric causal model and the interpretation of empirical evidence based on thought experiments that was developed by Ragnar Frisch and Trygve Haavelmo. We compare the econometric causal model with two currently popular causal frameworks: the Neyman–Rubin causal model and the Do-Calculus. The Neyman–Rubin causal model is based on the language of potential outcomes and was largely developed by statisticians. Instead of being based on thought…

使用多个工具变量进行政策评估AI 解读

Marginal treatment effect methods are widely used for causal inference and policy evaluation with instrumental variables. However, they fundamentally rely on the well-known monotonicity (threshold-crossing) condition on treatment choice behavior. This condition cannot hold with multiple instruments unless treatment choice is effectively homogeneous. We develop a new marginal treatment effect framework under a weaker, partial monotonicity condition. The partial monotonicity…

人力资本与迁移：一个警示性案例AI 解读

We analyze the impact that the option of migration might have on human capital accumulation. We show that, when the return to education for migrants is lower in the destination than in the origin, the overall incentive to accumulate human capital is reduced as restrictions on migration are relaxed. We use panel data from the Chinese Household Income Project to document that the return to education for rural individuals is lower in urban areas than in rural areas. We then use…

处理不完全随机化：HighScope Perry 学前项目的推断AI 解读

This paper considers the problem of making inferences about the effects of a program on multiple outcomes when the assignment of treatment status is imperfectly randomized. By imperfect randomization we mean that treatment status is reassigned after an initial randomization on the basis of characteristics that may be observed or unobserved by the analyst. We develop a partial identification approach to this problem that makes use of information limiting the extent to which…

私人退休账户制度中的性别养老金差距：家庭劳动供给与储蓄动态模型AI 解读

This paper develops and estimates a dynamic model of individuals’ and couples’ labor supply, savings, and retirement decisions to analyze how the design of Chile’s privatized pension system and a reform undertaken in 2008 affect gender pension gaps and old-age poverty. Chile has one of the longest-running private retirements accounts systems in the world, which has served as a model for many countries. The paper estimates the dynamic model using pre-reform data and compares…

2023年 Denis Sargan 计量经济学奖AI 解读

The editors1 of The Econometrics Journal decided to award the 2023 Denis Sargan Econometrics Prize to Alexander Kreiss (Leizpig University) for his article “Inference in regression discontinuity designs with high-dimensional covariates” with Christoph Rothe in the May 2023 issue of The Econometrics Journal.2 The regression-discontinuity approach can be used to learn about the causal effects of a binary treatment (e.g. enrolment in an educational program) that is fully…