论文检索

反倾销关税的就业后果：来自巴西的经验AI 解读

Abstract How do import tariffs affect employment? We develop an empirical strategy to identify the effects of tariffs using a difference-in-differences strategy, comparing antidumping (AD) investigations resulting in AD tariffs to those not resulting in AD tariffs. We find that an AD tariff decreases imports and increases employment in the protected sector. Moreover, employment in downstream firms decreases, while upstream firms are unaffected because the protected sector…

干扰与复杂实验设计下的优化方差估计AI 解读

Unbiased and consistent variance estimators generally do not exist for design-based treatment effect estimators because experimenters never observe more than one potential outcome for any unit. The problem is exacerbated by interference and complex experimental designs. Experimenters must accept conservative variance estimators in these settings, but they can strive to minimize the conservativeness. In this paper, we show that the task of constructing a minimally…

全球化与发展阶梯：推向顶端还是困于底部？AI 解读

Abstract We study the relationship between international trade and development in a model where countries differ in their capability, goods differ in their complexity, and capability growth is a function of a country’s pattern of specialization. Theoretically, we show that it is possible for international trade to increase capability growth in all countries and, in turn, to push all countries up the development ladder. This occurs if (i) shifting employment towards more…

失业保险潜在领取期限变化的微观与宏观效应AI 解读

Abstract We quantify micro and macro effects of changes in the potential benefit duration (PBD) in unemployment insurance. In Poland, the PBD is 12 months for the newly unemployed if the previous year’s county unemployment rate is more than 150% of the national average, and 6 months otherwise. We exploit this cut-off using regression discontinuity estimates on registry data containing the universe of unemployed from 2005 to 2019. For those whose PBD is directly affected by…

高维混杂下条件平均处理效应的非参数估计AI 解读

SUMMARY This study considers the practically important case of nonparametrically estimating heterogeneous average treatment effects that vary with a limited number of discrete and continuous covariates in a selection-on-observables framework where the number of possible confounders is very large. We propose a two-step estimator for which the first step is estimated by machine learning. We show that this estimator has desirable statistical properties such as consistency,…

工具变量单调性的识别内容AI 解读

SUMMARY: The existing literature has established that the instrument monotonicity assumption of Imbens and Angrist (1994) has no identifying content on the average treatment effect and more generally on the marginal distributions of potential outcomes in the sense that additionally imposing this assumption does not affect the identified set for the parameters of interest under instrument exogeneity whenever these assumptions are consistent with the data. This paper…

SUMMARY This paper extends difference-in-differences (DiD) to settings with continuous treatments. Specifically, the average treatment effect on the treated (ATT) at any level of treatment intensity is identified under a conditional parallel trends assumption. Estimating the ATT in this framework requires first estimating infinite-dimensional nuisance parameters, particularly the conditional density of the continuous treatment, which can introduce substantial bias. To…

二元结果高维广义线性模型中的因果推断AI 解读

Summary This paper proposes a debiased estimator for causal effects in high-dimensional generalized linear models with binary outcomes and general link functions. The estimator augments a regularized regression plug-in with weights computed from a single convex optimization that approximately balances link-derivative-weighted covariates and controls variance; it does not rely on estimated propensity scores. Under standard conditions, the estimator is $\sqrt{n}$-consistent…

跨异质地点的因果效应迁移估计AI 解读

We consider the problem of extrapolating treatment effects across heterogeneous populations (“sites”/“contexts”). We consider an idealized scenario in which the researcher observes cross-sectional data for a large number of units across several “experimental” sites in which an intervention has already been implemented to a new “target” site for which a baseline survey of unit-specific, pre-treatment outcomes and relevant attributes is available. Our approach treats the…

结合目标学习推断的在线元层级自适应设计框架AI 解读

Adaptive designs are increasingly used in clinical trials and online experiments to improve participant outcomes by dynamically updating treatment allocation as data accumulate. In practice, experimenters often consider multiple candidate designs, each with distinct trade-offs. However, typically only one design is implemented at a time, leaving benefits and costs of alternative designs unobserved and unquantified. To address this, we propose a novel meta-level adaptive…

因果 K 均值聚类AI 解读

Abstract Causal effects are often characterized at the population level, which can mask important heterogeneity across latent subgroups. Since the subgroup structure is unknown, identifying and evaluating subgroup specific effects is substantially more challenging than standard population level analysis. We address this problem by proposing Causal k-Means Clustering, a framework that uses k-means clustering ideas to recover unknown subgroup structure from individual level…

随机实验中协变量平衡的高斯化设计优化AI 解读

Abstract Achieving covariate balance in randomized experiments enhances the precision of treatment effect estimation. However, existing methods often require heuristic adjustments based on domain knowledge and are primarily developed for binary treatments. This paper presents Gaussianized Design Optimization, a novel framework for optimally balancing covariates in experimental design. The core idea is to Gaussianize the treatment assignments: we model treatments as…

模型误设下的稳健通用推断AI 解读

Summary In statistical inference, it is rarely realistic to assume that the hypothesized statistical model is well specified; consequently, it is important to understand the effects of misspecification on inferential procedures. When the hypothesized statistical model is misspecified, the natural target of inference is a projection of the data-generating distribution onto the model. We present a general method for constructing valid confidence sets for such projections,…

正则化广义协方差估计量AI 解读

Summary This paper proposes the Regularized Generalized Covariance (RGCov) estimator, a ridge-type extension of the Generalized Covariance (GCov) estimator for high-dimensional stationary time series. By regularizing the GCov objective function, which involves the inverse of the covariance matrix, RGCov improves numerical stability while preserving positive definiteness. Under suitable conditions, the new estimator is consistent, asymptotically normal, and…

基于深度分位数过程回归的分布性离策略评估AI 解读

This paper investigates the off-policy evaluation (OPE) problem from a distributional perspective. Rather than focusing solely on the expectation of the total return, as in most existing OPE methods, we aim to estimate the entire return distribution. To this end, we introduce a quantile-based approach for OPE using deep quantile process regression, presenting a novel algorithm called Deep Quantile Process regression-based Off-Policy Evaluation (DQPOPE). We provide new…

概念漂移下的学习：混杂、不变性与降维AI 解读

Practitioners often face the challenge of deploying prediction models in new environments with shifted distributions of covariates and responses. With observational data, such shifts are often driven by unobserved confounding, and can in fact alter the concept of which model is best. This paper studies distribution shifts in the domain adaptation problem with unobserved confounding. We postulate a linear structural causal model to account for endogeneity and unobserved…

观察性研究中用于因果推断的人工智能贝叶斯生成模型AI 解读

Causal inference in observational studies with high-dimensional covariates presents significant challenges. We introduce CausalBGM, an AI-powered Bayesian generative modeling approach that captures the causal relationship among covariates, treatment, and outcome. The core innovation is to estimate the individual treatment effect (ITE) by learning the individual-specific distribution of a low-dimensional latent feature set (e.g., latent confounders) that drives changes in…

析因双重差分AI 解读

We formulate factorial difference-in-differences (FDID), a research design that extends canonical difference-in-differences (DID) to settings in which an event affects all units. In many panel data applications, researchers exploit cross-sectional variation in a baseline factor alongside temporal variation in the event, but the corresponding estimand is often implicit and the justification for applying the DID estimator remains unclear. We frame FDID as a factorial design…

人机交互的收益：混杂环境中的超级策略学习AI 解读

As AI becomes more prevalent throughout society, effective methods of integrating humans and AI systems that leverage their respective strengths and mitigate risk have become an important priority. In this article, we introduce the paradigm of super policy learning that takes advantage of Human-AI interaction for data driven sequential decision making. This approach uses the observed action, either from AI or humans, as input for achieving a stronger oracle in policy…

基于汇总数据、消除赢家诅咒的稳健孟德尔随机化AI 解读

In the past decade, the increased availability of genome-wide association studies summary data has popularized Mendelian Randomization (MR) for conducting causal inference. MR analyses, incorporating genetic variants as instrumental variables, are known for their robustness against reverse causation bias and unmeasured confounders. Nevertheless, classical MR analyses using summary data may still produce biased causal effect estimates due to the winner's curse and pleiotropy…

工作稳定性对意大利货币贫困的影响：因果小区域估计AI 解读

Job stability – encompassing secure contracts, adequate wages, social benefits, and career opportunities – is a critical determinant in reducing monetary poverty, as it provides households with reliable income and enhances economic well-being. This study draws on EU-SILC survey and census data to estimate the causal effect of job stability on monetary poverty across Italian provinces, quantifying its influence, and analyzing regional disparities. We introduce a novel causal…

多重矩等式检验的功效增强：超越二范数与无穷范数AI 解读

Tests based on the 2- and ∞-norm have received considerable attention in high-dimensional testing problems, as they are powerful against dense and sparse alternatives, respectively. The power enhancement principle of Fan et al. (2015) combines these two norms to construct improved tests that are powerful against both types of alternatives. In the context of testing whether a candidate parameter satisfies a large number of moment equalities, we construct tests that harness…

Riesz 表示元的对抗估计AI 解读

Many causal parameters are linear functionals of an underlying regression. The Riesz representer is a key component in the asymptotic variance of a semiparametrically estimated linear functional. We propose an adversarial framework to estimate the Riesz representer using general function spaces. We prove a nonasymptotic mean square rate in terms of an abstract quantity called the critical radius, then specialize it for neural networks, random forests, and reproducing kernel…

借助辅助总体工具变量识别与多重稳健估计因果效应AI 解读

Estimating causal effects in a target population with unmeasured confounders is challenging, especially when instrumental variables (IVs) are unavailable. However, IVs from auxiliary populations with similar problems can help infer causal effects in the target population. While the homogeneous conditional average treatment effect assumption has been widely used for effect transportability, it has not been explored in IV-based data fusion. We include it as a basic approach,…