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利用数字孪生微观模拟与 Q 学习制定公共卫生政策：新冠加强针案例AI 解读

The COVID-19 pandemic highlighted the urgent need for effective vaccine policies, but traditional clinical trials often lack sufficient data to capture the diverse population characteristics necessary for comprehensive public health strategies. Ethical concerns around randomized trials during a pandemic further complicate policy development for public health. Reinforcement Learning (RL) offers a promising alternative for vaccine policy development. However, direct online RL…

Abstract Adaptive experiments use preliminary analyses of the data to inform further course of action and are commonly used in many disciplines including medical and social sciences. Because the null hypothesis and experimental design are data-dependent, it has long been recognized that statistical inference for adaptive experiments is not straightforward. Most existing methods only apply to specific adaptive designs and rely on strong assumptions. In this work, we propose…

线性模型中利用异方差进行稳健推断AI 解读

Summary We study inference via heteroskedasticity in linear models commonly used for macroeconomic policy analysis, where covariate endogeneity must often be addressed with limited time and data. Our framework nests standard heteroskedasticity-based approaches, allows for new non-nested restrictions, and does not require ex ante regime labelling. We propose an easily implementable weak identification robust test and derive sufficient conditions for its validity. Simulation…

合成控制的识别与推断：近端因果推断框架AI 解读

Synthetic control (SC) methods are commonly used to estimate the treatment effect on a single treated unit in panel data settings. An SC is a weighted average of control units built to match the treated unit, with weights typically estimated by regressing pre-treatment outcomes and measured covariates of the treated unit to those of the control units. However, the classical SC method was primarily proposed for empirical settings where a good pre-treatment fit is attainable…

一致有界邻域干扰下的因果推断AI 解读

Abstract Randomized experiments remain the gold standard for estimating treatment effects; however, network interference compromises the validity of traditional estimators by violating the stable unit treatment value assumption and introducing bias. Although cluster-randomized designs help mitigate some bias, they struggle to accommodate complex network structures and cannot disentangle direct from indirect effects. To address these challenges, we develop a design-based…

收入不平等与就业创造AI 解读

Abstract We propose a novel channel through which rising income inequality affects job creation and macroeconomic outcomes. High-income households save relatively more in stocks and bonds but less in bank deposits. A rising top income share thereby increases the relative financing cost for bank-dependent firms, which in turn create fewer jobs compared to other firms. Exploiting variation in top income shares across US states and an instrumental variable strategy, we provide…

迈向个体层面数据融合的统一半参数理论AI 解读

We consider inference about a finite-dimensional parameter integrating samples from independent sources. A recently developed theory considers scenarios where sources align with subsets of the conditional distributions of a single factorization of the joint target distribution. While this theory applies in many settings, it falls short in important data fusion problems, such as two-sample instrumental variable analysis, settings that integrate data from epidemiological…

以史为鉴：事件前后收益横截面的统计推断AI 解读

Confounding events can cause false positives when testing the relationship between short-term returns around a quasi-experimental event and firm characteristics. We show that this risk is severe in practice — return-characteristic relationships are often statistically significant at the 1% level on over 30% of all trading days. Benchmarking a relationship against the distribution of the same relationship on pre-event days is effective at addressing the problem. We introduce…

有限样本多处理决策的极小极大遗憾规则札记AI 解读

We study minimax regret treatment rules under matched treatment assignment in a setup where a policymaker, informed by a sample of size N , needs to decide between T different treatments for a T ≥ 2 $T\geq 2$ upper T greater than or equals 2 . Randomized rules are allowed for. We show that the generalization of the minimax regret rule derived in Schlag (2006, ELEVEN—Tests needed for a recommendation , EUI working paper) and Stoye (2009, Journal of Econometrics 151, 70–81)…

没有工具变量时的边际处理效应AI 解读

ABSTRACT We propose a method for defining, identifying, and estimating the marginal treatment effect (MTE) without imposing the instrumental variable (IV) assumptions of independence, exclusion, and separability (or monotonicity). Under a new definition of the MTE based on reduced‐form treatment error that is statistically independent of the covariates, we find that the relationship between the MTE and standard treatment parameters holds in the absence of IVs. We provide a…

缓冲储蓄行为的统计推断AI 解读

ABSTRACT There is an ongoing debate about the buffer‐stock saving hypothesis. This paper develops a formal test of the hypothesis by examining the sub‐unity of the marginal propensity to consume out of permanent income (MPCP). Our test relies on a novel estimator of the average derivative of a nonparametric consumption model, using constructed measures of latent permanent income shocks that accommodate nonparametric income distributions. Following Li and Vuong (1998), we use…

双重 LASSO：复现与实践启示AI 解读

ABSTRACT The rise of machine learning (ML) is one of the most prominent developments in applied econometrics in the past decade. The focus of much economic analysis is causal, rather than prediction, and Belloni et al. (2014) demonstrate how ML methods can be used in causal inference. This paper undertakes a narrow and wide replication of the Monte Carlo and empirical examples presented by Belloni et al. (2014). We discuss practical implications of this replication for the…

公立学校经费、学校质量与成年后的犯罪AI 解读

Abstract This paper asks whether increasing public school funding can be an effective long-run crime-prevention strategy in the United States. Specifically, we examine the effect of increases in funding early in children’s lives on the likelihood that they are arrested as adults. We exploit quasi-experimental variation in public school funding, leveraging two natural experiments in Michigan and a novel administrative data set linking the universe of Michigan public school…

不完全依从的协变量自适应随机化中，利用回归调整提高估计效率AI 解读

Abstract We investigate how to improve efficiency using regression adjustments with covariates in covariate-adaptive randomizations (CARs) with imperfect subject compliance. Our regression-adjusted estimators, which are based on the doubly robust moment for local average treatment effects, are consistent and asymptotically normal even with heterogeneous probabilities of assignment and misspecified regression adjustments. We propose an optimal but potentially misspecified…

从诊断性检验看论文发表过程中的选择压力AI 解读

Abstract Economics papers increasingly report balance, pretrend, placebo, and other “sniff tests,” rejection of which is bad news for authors, undermining the credibility of their main results. We derive nonparametric bounds on the latent proportion of significant sniff tests removed by the publication process (whether by p-hacking or relegation to the file drawer) and the proportion whose significance was due to true misspecification, not bad luck. Using a hand-collected…

基于潜在因子模型的个体化动态中介分析AI 解读

Mediation analysis plays a crucial role in causal inference as it can investigate the pathways through which treatment influences outcome. Most existing mediation analysis assumes that mediation effects are static and homogeneous within populations. However, mediation effects usually change over time and exhibit significant heterogeneity among individuals in many real-world applications. Additionally, the mediation mechanism can be complicated and involves non-sparse, making…

单调连续处理效应曲线的双重稳健逐点置信区间AI 解读

We study nonparametric inference for the causal dose–response curve when the treatment variable is continuous rather than discrete. We develop doubly robust confidence intervals for the continuous treatment effect curve (at a fixed point) under the assumption that it is monotonic, based on inverting a likelihood ratio-type test. Monotonicity of the treatment effect curve is often a very natural assumption, and this assumption removes the need to choose a smoothing or tuning…

结果延迟下的协变量调整响应自适应设计AI 解读

Covariate-adjusted response-adaptive (CARA) designs have gained widespread adoption for their clear benefits in enhancing experimental efficiency and participant welfare. These designs dynamically adjust treatment allocations during interim analyses based on participant responses and covariates collected during the experiment. However, delayed responses can significantly compromise the effectiveness of CARA designs, as they hinder timely adjustments to treatment assignments…

不同实验中个体处理效应分布与分位数的增强推断AI 解读

Understanding treatment effect heterogeneity has become increasingly important in many fields. In this paper we study distributions and quantiles of individual treatment effects to provide a more comprehensive and robust understanding of treatment effects beyond usual averages, despite they are more challenging to infer due to nonidentifiability from the observed data. Recent randomization-based approaches offer finite-sample valid inference for treatment effect…

二元结局下的个体风险量化AI 解读

Abstract Understanding treatment effect heterogeneity is crucial for reliable decision-making in treatment evaluation and selection. The conditional average treatment effect (CATE) is widely used to capture treatment effect heterogeneity induced by observed covariates and to design individualized treatment policies. However, it is an average metric within subpopulations, which prevents it from revealing individual risk, potentially leading to misleading results. This article…

轻度每日饮酒与寿命观察性研究中证据因子的新构造AI 解读

Does light daily alcohol consumption lengthen life? In any observational study of this question, we expect drinking behavior—that is, treatment assignment—to be confounded by measured and unmeasured covariates. An observational study has two evidence factors if there are two essentially independent tests of the same null hypothesis about causal effects, where the two tests are susceptible to different unmeasured biases in treatment assignment. Because they are independent,…

通胀、战争债券与1950年代的选民反弹AI 解读

Abstract We study the role of war bonds and inflation in post-WWII federal elections. After WWII, major bouts of inflation in 1946-48 and 1950-51 depressed the real returns of the bonds sold to households during the war. In a difference-in-differences framework, we find that counties with higher war bond purchases shifted their votes towards the Republican Party in postwar elections. To address the endogeneity of war bond purchases, we use an instrumental variables design,…

收购后的工作转换与雇员收入：连接企业和劳动者结果AI 解读

Abstract This paper connects changes in employer characteristics through job transitions to employee earnings following mergers and acquisitions. Using firm balance sheet data linked to individual earnings data in Canada and a matched difference-in-differences design, we find that earnings of workers at target firms decrease after M&As relative to control workers, largely driven by those who move to other firms. Workers leaving targets move to larger and more profitable…

企业对招聘与投资补贴的反应：加州竞争税收抵免的断点证据AI 解读

Abstract We examine firm responses to state hiring and investment subsidies. We leverage institutional features of the California Competes Tax Credit (CCTC), a large-scale business incentive program that incorporates best practices from prior job creation policies. The CCTC award selection procedure combines formula-based and discretionary components. Leveraging applicant score eligibility cutoffs in a regression discontinuity design and taking advantage of rich longitudinal…