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因果工具变量模型中的贝叶斯模型平均AI 解读

ABSTRACT Instrumental variables are a popular tool to infer causal effects under unobserved confounding, but choosing suitable instruments is challenging in practice. We propose gIVBMA, a Bayesian model averaging procedure that addresses this challenge by averaging across different sets of instrumental variables and covariates in a structural equation model. This allows for data‐driven selection of valid and relevant instruments and provides additional robustness against…

无混杂与共同趋势的联合检验AI 解读

ABSTRACT We introduce an overidentification test of two alternative assumptions to identify the average treatment effect on the treated in a two‐period panel data setting: unconfoundedness and common trends. Under unconfoundedness, treatment assignment and post‐treatment outcomes are independent, conditional on control variables and pre‐treatment outcomes, which motivates including the pre‐treatment outcomes in the set of controls. Under common trends, the trend and the…

自适应随机实验中的最优协变量离散化AI 解读

ABSTRACT This paper introduces a novel method to adaptively design randomized experiments. For randomized experiments with a pilot stage, or multistage experiments, Hahn et al. (2011) propose an adaptive experimental design that adjusts the next stage's propensity score based on data from the previous stages. This paper discusses how the discretization of covariates affects the precision of the estimation of the average treatment effect (ATE) through the estimated propensity…

回归不连续效应及其外部有效性的联合推断AI 解读

ABSTRACT The external validity of regression discontinuity designs is crucial for informing policy but is rarely examined in applied work. To advance empirical practice, we propose a joint inference procedure for the treatment effect and its local external validity, captured by the treatment effect derivative (TED), within a robust bias correction framework. We further introduce a locally linear treatment effects assumption, which extends the scope of the TED and enables…

以倾向得分校准改善双重／去偏机器学习的平均处理效应有限样本估计AI 解读

ABSTRACT Double/debiased machine learning (DML) uses for estimating an average treatment effect (ATE) a double‐robust score function that relies on the prediction of nuisance functions, such as the propensity score, which is the probability of treatment assignment given covariates. Estimators relying on double‐robust score functions are highly sensitive to errors in propensity score predictions. Machine learning algorithms have been found to produce models that often…

含时变协变量的直接与间接处理效应AI 解读

ABSTRACT We propose a simple approach to treatment effect estimation in panel data that is valid when the number of time periods is small and the parallel trends condition is violated due to the presence of interactive fixed effects. The procedure allows the covariates to be affected by treatment and enables separation of the part of the estimated treatment effect that is due to the covariates from the part that is not. The asymptotic properties of the new approach are…

宏观审慎政策的尾部传导：叙事识别证据AI 解读

ABSTRACT We estimate the causal effects of macroprudential policies on the entire distribution of GDP growth for advanced European economies using a narrative‐identification strategy in a quantile‐regression framework. While macroprudential policy has near‐zero effects on the center of the GDP‐growth distribution, tighter policy brings benefits by reducing the variance of future growth, significantly boosting the left tail while simultaneously reducing the right. Assessing a…

一般处理模型的高阶去偏估计量AI 解读

We have witnessed tremendous progress in developing the foundation for econometrics and causal inference in the past decades. The most popular paradigm is the classical (first-order) semiparametric theory, in which a key building block is the (first-order) influence function. However, it is well known that estimators based on influence functions can be sub-optimal in various settings. To address this issue, higher-order influence functions (HOIF) are developed. However, most…

中介分析中复合原假设的最优检验AI 解读

The indirect effect of an exposure on an outcome through an intermediate variable can be identified by a product of two regression coefficients under certain causal and regression modeling assumptions. In this context, the null hypothesis of no indirect effect is a composite null hypothesis, as the null holds if either regression coefficient is zero. A consequence is that traditional hypothesis tests are severely underpowered near the origin (i.e., when both coefficients are…

生成式人工智能辅助因果推断：以文本作为处理AI 解读

In this paper, we demonstrate how to enhance the validity of causal inference with unstructured high-dimensional treatments like texts, by leveraging the power of generative Artificial Intelligence (GenAI). Specifically, we propose to use a deep generative model such as large language models (LLMs) to efficiently generate treatments and use their internal representation for subsequent causal effect estimation. We show that the knowledge of this true internal representation…

通过剖面化迁移学习消除混杂AI 解读

Unmeasured confounders are a major source of bias in regression-based effect estimation and causal inference. In this paper, we propose a new profiled transfer learning framework, ProTrans, to address confounding effects in the target dataset, when additional source datasets with similar confounding structures are available. We introduce the concept of profiled residuals to characterize the shared confounding patterns between source and target datasets. By incorporating…

部分识别下的稳健贝叶斯处理选择AI 解读

We study a class of binary treatment choice problems with partial identification through the lens of robust (multiple prior) Bayesian analysis. We use a convenient set of prior distributions to derive ex-ante and ex-post robust Bayes decision rules, both for decision makers who can randomize and for decision makers who cannot. Our main messages are as follows: First, ex-ante and ex-post robust Bayes decision rules do not agree in general, whether or not randomized rules are…

用于实验设计的合成控制方法AI 解读

Abstract This paper studies how to design experiments when the experimental units are large aggregate entities (e.g., markets), and only one or a small number of units can be treated. We propose a class of non-randomized experimental designs using the synthetic control method, which jointly selects treated units and untreated units to serve as a comparison group. We analyze the properties of estimators under these synthetic control designs and develop new inferential…

沙威的怀疑有道理吗？持续时间结果的边际处理效应AI 解读

Abstract We identify the distributional and quantile marginal treatment effect functions when the outcome is right-censored. Our method requires a conditionally exogenous instrument and random censoring. We propose asymptotically consistent semi-parametric estimators and valid inferential procedures for the target functions . To illustrate, we evaluate the effect of alternative sentences (fines and community service vs. no punishment) on recidivism in Brazil. Our results…

企业集中度与工作设计：弹性工时安排的案例AI 解读

Abstract We study how employer concentration affects job design, focusing on schedule flexibility as a non-wage job attribute. Using the full text of 46 million UK job vacancies from 2014–2019, we construct new measures of flexible work arrangements using supervised machine learning and large-language-model—assisted annotations. Adopting an instrumental variable strategy using a Bartik-style prediction of the change in employer concentration based on nationwide hiring…

跨期处理效应的双重差分估计AI 解读

Abstract We study treatment-effect estimation using panel data. The treatment may be nonbinary, nonabsorbing, and the outcome may be affected by treatment lags. We make a parallel-trends assumption and propose event-study estimators of the effect of being exposed to a weakly higher treatment dose for ℓ periods. We also propose normalized estimators that estimate a weighted average of the effects of the current treatment and its lags. We also analyze commonly used two-way…

多重随机化设计：存在干扰时的估计与推断AI 解读

Abstract Completely randomized experiments, originally developed by Fisher and Neyman in the 1930s, are still widely used in practice, even in online experimentation. However, such designs are of limited value for answering standard questions in marketplaces, where multiple populations of agents interact strategically, leading to complex patterns of spillover effects. In this article, we derive the finite-sample properties of tractable estimators for ‘Simple Multiple…

弱识别函数的强识别泛函推断AI 解读

Abstract In a variety of applications, including nonparametric instrumental variable (NPIV) analysis, proximal causal inference under unmeasured confounding, and analysis of missing-not-at-random data with shadow variables, we are interested in inference on a continuous linear functional (e.g. average causal effects) of nuisance functions (e.g. NPIV regression) defined by conditional moment restrictions. These nuisance functions are often weakly identified, meaning the…

Abstract Causal inference on time series data is a challenging problem, especially in the presence of unobserved confounders. In this work, we focus on estimating the causal effect of a multivariate time series on a univariate time series when a third (possibly multivariate) time series confounds the relationship but remains unobserved. By assuming spectral sparsity of the confounder, we show how this problem can be framed as an adversarial outlier problem in the frequency…

带颜色约束的高斯有向无环图模型AI 解读

Abstract We study submodels of Gaussian directed acyclic graph (DAG) models defined by partial homogeneity constraints imposed on the model error variances and structural coefficients. We represent these models with coloured DAGs and investigate their properties for use in statistical and causal inference. Local and global Markov properties are provided and shown to characterize the coloured DAG model. Additional properties relevant to causal discovery are studied, including…

作为线性回归的增广平衡权重AI 解读

Abstract We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning. These popular doubly robust estimators combine outcome modelling with balancing weights—weights that achieve covariate balance directly instead of estimating and inverting the propensity score. When the outcome and weighting models are both linear in some (possibly infinite) basis, we show that the augmented estimator is equivalent to a single…

通过干扰平衡设计网络实验AI 解读

Randomized experiments are widely used to estimate the causal effects of treatment or intervention across various scientific fields. Nowadays, numerous experiments are conducted in network settings where the outcome of one unit depends not only on its own treatment but also on the treatments of other units within the network. This phenomenon, known as network interference, poses significant challenges for experimental design. Existing randomization methods often neglect…

我的员工值多少？薪酬对标的影响AI 解读

Abstract Firms are allowed to use aggregate data on market salaries to set pay, a practice known as salary benchmarking. Using national payroll data, we study firms that gain access to a tool that reveals market benchmarks for each job title. Using a difference-in-differences design, we find that the benchmark information reduces salary dispersion by 25%. Thus, salary dispersion must stem partly from aggregate uncertainty about the salaries offered by other firms. Our model…

替代指标指数：结合短期代理更快、更精确地估计长期处理效应AI 解读

Abstract A common challenge in estimating the impact of interventions (e.g. job training programmes, educational programmes) is that many outcomes of interest (e.g. lifetime earnings or other labour market outcomes) are observed with a long delay. In biomedical settings, this is often addressed by using short-term outcomes as so-called “surrogates” for the outcome of interest, e.g. tumour size as a surrogate for mortality in cancer studies. We build on this literature by…