每日简报

评价时变处理效应的随机化方法AI 解读

Tests for paired censored outcomes have been extensively studied, with some justified in the context of randomization-based inference. These tests are primarily designed to detect an overall treatment effect across the entire follow-up period, providing limited insight into when the effect manifests and how it changes over time. In this article, we introduce new randomization-based tests for paired censored outcomes that enable both time-specific and long-term analysis of a…

因果发现优于随机猜测吗？评估算法时使用负对照AI 解读

New proposals for causal discovery algorithms are typically evaluated using simulations and a few selected real data examples with known data generating mechanisms. However, there does not exist a general guideline for how such evaluation studies should be designed, and therefore, comparing results across different studies can be difficult. In this article, we propose to use negative controls as a common evaluation baseline by posing the question: Are we doing better than…

交错实施下的推断：以《平价医疗法案》为例AI 解读

Panel data consists of a collection of $N$ units that are observed over $T$ units of time. A policy or treatment is subject to staggered adoption if different units take on treatment at different times and remains treated (or never at all). Assessing the effectiveness of such a policy requires estimating the treatment effect, corresponding to the difference between outcomes for treated versus untreated units. We develop inference procedures that build upon a computationally…

患者加权存活期间估计目标的非参数估计AI 解读

In clinical trials with recurrent events, such as repeated hospitalizations terminating with death, it is important to consider the patient events overall history for a thorough assessment of treatment effects. The occurrence of fewer events due to early deaths can lead to misinterpretation, emphasizing the importance of a while-alive strategy as suggested in Schmidli et al. (2023). In this study, we focus on the patient weighted while-alive estimand, represented as the…

约束条件下因果效应的可识别性AI 解读

We study the identification of causal effects in the presence of different types of constraints (e.g., logical constraints) in addition to the causal graph. These constraints impose restrictions on the models (parameterizations) induced by the causal graph, reducing the set of models considered by the identifiability problem. We formalize the notion of constrained identifiability, which takes a set of constraints as another input to the classical definition of…

时变不可观测异质性离散化后的推断AI 解读

Approximating time-varying unobserved heterogeneity by discrete types has become increasingly popular in economics. Yet, provably valid post-clustering inference for target parameters in models that do not impose an exact group structure is still lacking. This paper fills this gap in the leading case of a linear panel data model with nonseparable two-way unobserved heterogeneity. Building on insights from the double machine learning literature, we propose a simple inference…

以双参数敏感性分析整合随机与非随机研究AI 解读

Causal inference is vital for informed decision-making across fields such as biomedical research and social sciences. Randomized controlled trials (RCTs) are considered the gold standard for internal validity of inferences, whereas observational studies (OSs) often provide the opportunity for greater external validity. However, both data sources have inherent limitations preventing their use for broadly valid statistical inferences: RCTs may lack generalizability due to…

通过高效非凸优化学习因果不变性AI 解读

Identifying the causal relationship among variables from observational data is an important yet challenging task. This work focuses on identifying the direct causes of an outcome and estimating their magnitude, i.e., learning the causal outcome model. Data from multiple environments provide valuable opportunities to uncover causality by exploiting the invariance principle that the causal outcome model holds across heterogeneous environments. Based on the invariance…

基于部分处理历史的边际结构模型：时变处理效应估计AI 解读

Inverse probability (IP) weighting of marginal structural models (MSMs) can provide consistent estimators of time-varying treatment effects under correct model specifications and identifiability assumptions, even in the presence of time-varying confounding. However, this method has two problems: (i) inefficiency due to IP-weights cumulating all time points and (ii) bias and inefficiency due to the MSM misspecification. To address these problems, we propose (i) new IP-weights…

用于因果中介与溢出效应的网络结构方程模型AI 解读

Social network interference induces complex dependencies where a unit's outcome is influenced not only by its own exposure and mediator but also by those of connected neighbors. In such settings, a significant challenge lies in distinguishing direct exposure effects from interference-driven spillover effects, and further separating these from indirect effects mediated by intermediate variables. To address this, we propose a theoretical framework utilizing structural…

随机效应面板模型中的参数特定偏差诊断AI 解读

The Hausman specification test assesses the random-effects specification by comparing the random-effects estimator with a fixed-effects alternative. This note shows how a recently proposed bias diagnostic for linear mixed models can complement that test in random-effects panel-data applications. The diagnostic delivers parameter-specific internal estimates of finite-sample bias, together with permutation-based $p$-values, from a single fitted random-effects model. We…

结合 BART 与主分层估计避孕使用对就业的影响：尼日利亚与塞内加尔AI 解读

There is interest in learning about the causal effects of modern contraceptive use on empowerment outcomes. Data on this question often come from family planning (FP) programs that increase access to FP and facilitate contraceptive use among some women, rather than directly assigning use. Women whose contraceptive behavior changes because of these programs ("compliers") may differ from target populations in ways that alter the consequences of contraceptive use for…

通过广义贝叶斯对参数模型进行自适应非参数扰动AI 解读

Parametric Bayesian modeling offers a powerful and flexible toolbox for machine learning. Yet the model, however detailed, may still be wrong, and this can make inferences untrustworthy. In this paper we introduce a new class of semiparametric corrections for parametric Bayesian models, when the target of inference is a functional of the true data distribution. Our starting point is a fully Bayesian modeling approach, which explicitly accounts for the possibility that the…

多地点异质性处理效应推广的极小极大遗憾估计AI 解读

To test scientific theories and develop individualized treatment rules, researchers often wish to learn heterogeneous treatment effects that can be consistently found across diverse populations and contexts. We consider the problem of generalizing heterogeneous treatment effects (HTE) based on data from multiple sites. A key challenge is that a target population may differ from the source sites in unknown and unobservable ways. This means that the estimates from…

半参数贝叶斯双重差分AI 解读

This paper studies semiparametric Bayesian inference for the average treatment effect on the treated (ATT) within the difference-in-differences (DiD) research design. We propose two new Bayesian methods with frequentist validity. The first one is the semiparametric Bayesian outcome regression, where we place a Gaussian process prior on the conditional mean function of the control group. The second method is a doubly robust Bayesian procedure that adjusts the prior…

处理与结果非随机缺失时，工具变量模型的可识别性AI 解读

The instrumental variable model of Imbens and Angrist (1994) and Angrist et al. (1996) identifies the local average treatment effect, also known as the complier average causal effect (CACE). In practice, however, the treatment and outcome are often missing, and when they are missing not at random (MNAR), the CACE is generally not identifiable without further assumptions, because the underlying data distribution itself cannot be recovered. We study when the CACE remains…

计数反向响应者：实验设计模型提供超越平均效应的证据AI 解读

Using only a binary intervention and outcome and the design of the randomization within an experiment, we construct a design-based likelihood of the joint distribution of potential outcomes in the sample -- the numbers of always takers, compliers, defiers, and never takers. We develop a visualization to show that samples with defiers can sometimes generate the data in more ways than samples without, yielding a higher likelihood. This likelihood can vary within the Frechet…

Ramsey 式空间交互函数线性检验AI 解读

We propose a computationally straightforward test for the linearity of a spatial interaction function. Such functions arise commonly, either as practitioner imposed specifications or due to optimizing behaviour by agents. Our conditional heteroskedasticity robust test is nonparametric, but based on the Lagrange Multiplier principle and reminiscent of the Ramsey RESET approach. This entails estimation only under the null hypothesis, which yields an easy to estimate linear…

时空因果推断中的异质性处理效应估计AI 解读

Scholars from diverse fields increasingly rely on high-frequency spatio-temporal data. Yet, causal inference with these data remains challenging due to spatial spillover and temporal carryover effects. We develop methods to estimate heterogeneous treatment effects by allowing for arbitrary spatial and temporal causal dependencies. We focus on common settings where the treatment and outcomes are time-varying spatial point patterns and where moderators are either spatial or…

含交互效应的超高维因果中介分析去偏估计AI 解读

Mediation analysis is a crucial tool for uncovering the mechanisms through which a treatment affects an outcome, providing deeper causal insights and guiding effective interventions. Despite substantial advances in mediation analysis with fixed- or low-dimensional mediators and covariates, estimation and inference for mediation functionals remain limited when both mediators and covariates are ultra-high-dimensional. In this paper, we propose an estimator for the mediation…

似然模型偶然参数问题的 Neyman 正交化方法AI 解读

A popular approach to perform inference on a target parameter in the presence of nuisance parameters is to construct estimating equations that are orthogonal to the nuisance parameters, in the sense that their expected first derivative is zero. Such first-order orthogonalization allows the estimator of the nuisance parameters to converge at a slower-than-parametric rate. It may, however, not suffice when the nuisance parameters are very imprecisely estimated. Leading…

断点回归设计中外推治疗效应的界AI 解读

External validity is a central concern in regression discontinuity (RD) designs. To assess treatment effect heterogeneity and the representativeness of the conventional RD estimand, this paper studies the identifying content of monotonicity and smoothness restrictions for treatment effects away from the cutoff in both canonical and multi-cutoff RD designs. In the canonical design, monotonicity in the running variable alone has limited identifying power and generally yields…

评估连续处理对生存结局影响的一类非参数方法AI 解读

In randomized trials and observational studies, it is often necessary to evaluate the extent to which an intervention affects a time-to-event outcome, which is only partially observed due to right censoring. For instance, in infectious disease studies, it is frequently of interest to characterize the relationship between risk of acquisition of infection with a pathogen and a biomarker previously measuring for an immune response against that pathogen induced by prior…

存在受试者协变量时受控实验的协同设计AI 解读

We consider the optimal experimental design problem of allocating subjects to treatment or control when subjects participate in multiple, separate controlled experiments within a short time-frame and subject covariate information is available. Here, in addition to subject covariates, we consider the dependence among the responses coming from the subject's random effect across experiments. In this setting, the goal of the allocation is to provide precise estimates of…