每日简报

只需询问两次：选择概率、事前收益与支付意愿的识别AI 解读

One of the exciting developments in the stated preference literature is the use of probabilistic stated preference experiments to estimate semi-parametric population distributions of ex ante returns and willingness-to-pay (WTP) for a choice attribute. This relies on eliciting several choices per individual, and estimating separate demand functions, at the cost of possibly long survey instruments. This paper shows that the distributions of interest can be recovered from at…

复杂实验的基于设计估计理论AI 解读

This paper considers the estimation of treatment effects in randomized experiments with complex experimental designs, including cases with interference between units. We develop a design-based estimation theory for arbitrary experimental designs. Our theory facilitates the analysis of many design-estimator pairs that researchers commonly employ in practice and provide procedures to consistently estimate asymptotic variance bounds. We propose new classes of estimators with…

Logistic 回归模型的聚类稳健刀切法与自助法推断AI 解读

We study cluster-robust inference for logistic regression (logit) models. Inference based on the most commonly-used cluster-robust variance matrix estimator (CRVE) can be very unreliable. We study several alternatives. Conceptually the simplest of these, but also the most computationally demanding, involves jackknifing at the cluster level. We also propose a linearized version of the cluster-jackknife variance matrix estimator as well as linearized versions of the wild…

如何抽样与何时停止：广义 Wald 问题及极小极大策略AI 解读

We study sequential experiments where sampling is costly and a decision-maker aims to determine the best treatment for full scale implementation by (1) adaptively allocating units between two possible treatments, and (2) stopping the experiment when the expected welfare (inclusive of sampling costs) from implementing the chosen treatment is maximized. Working under a continuous time limit, we characterize the optimal policies under the minimax regret criterion. We show that…

二元结局模型中的非线性变换内生性校正：识别与推断AI 解读

For binary outcome models, an endogeneity correction based on nonlinear rank-based transformations is proposed. Identification without external instruments is achieved under one of two assumptions: either the endogenous regressor is a nonlinear function of one component of the error term, conditional on the exogenous regressors, or the dependence between the endogenous and exogenous regressors is nonlinear. Under these conditions, we prove consistency and asymptotic…

推荐系统中潜在混杂因素的因果结构表示学习AI 解读

Inferring user preferences from the historical feedback of users is a valuable problem in recommender systems. Conventional approaches often rely on the assumption that user preferences in the feedback data are equivalent to the real user preferences without additional noise, which simplifies the problem modeling. However, there are various confounders during user-item interactions, such as weather and even the recommendation system itself. Therefore, neglecting the…

双重交叉拟合的双重稳健估计：超越级数回归AI 解读

Doubly robust estimators with cross-fitting have gained popularity in causal inference due to their favorable structure-agnostic error guarantees. However, when additional structure, such as Hölder smoothness, is available then more accurate "double cross-fit doubly robust" (DCDR) estimators can be constructed by splitting the training data and undersmoothing nuisance function estimators on independent samples. We study a DCDR estimator of the Expected Conditional…

多维未观测异质性下的政策相关处理效应AI 解读

This paper provides a unified framework for bounding policy relevant treatment effects using instrumental variables. In this framework, the treatment selection may depend on multidimensional unobserved heterogeneity. We derive bilinear constraints on the target parameter by extracting information from identifiable estimands. We apply a convex relaxation method to these bilinear constraints and provide conservative yet computationally simple bounds. Our convex-relaxation…

部分线性模型中二元工具变量的约束设计AI 解读

We study the question of how best to assign an encouragement in a randomized encouragement study. In our setting, units arrive with covariates, receive a nudge toward treatment or control, acquire one of those statuses in a way that need not align with the nudge, and finally have a response observed. The nudge can be modeled as a binary instrument if one assumes that it affects the response only via the treatment status. Our goal is to assign the nudge as a function of…

分层随机实验中的锐方差估计与因果自助法AI 解读

Randomized experiments are the gold standard for estimating treatment effects, and randomization serves as a reasoned basis for inference. In widely used stratified randomized experiments, randomization-based finite-population asymptotic theory enables valid inference for the average treatment effect, relying on normal approximation and a Neyman-type conservative variance estimator. However, when the sample size is small or the outcomes are skewed, the Neyman-type variance…

panelPomp：在 R 中利用部分可观测马尔可夫过程分析面板数据AI 解读

Panel data arise when time series measurements are collected from multiple, dynamically independent but structurally related systems. Each system's time series can be modeled as a partially observed Markov process (POMP), and the ensemble of these models is called a PanelPOMP. If the time series are relatively short, statistical inference for each time series must draw information from across the entire panel. The component systems in the panel are called units; model…

超越任意重复次数：因果推断模拟设计的原则性方法AI 解读

Evaluation of novel treatment effect estimators frequently relies on simulation studies lacking formal statistical comparisons and using arbitrary numbers of replications ($J$). This hinders reproducibility and efficiency. We propose the Test-Informed Simulation Count Algorithm (TISCA) to address these shortcomings. TISCA integrates Welch's t-tests with power analysis, iteratively running simulations until a pre-specified power (e.g., 0.8) is achieved for detecting a…

广义因果自助法设计的整数规划AI 解读

In experimental causal inference, we distinguish between two sources of uncertainty: design uncertainty, due to the treatment assignment mechanism, and sampling uncertainty, when the sample is drawn from a super-population. This distinction matters in settings with small fixed samples and heterogeneous treatment effects, as in geographical experiments. The standard bootstrap procedure most often used by practitioners primarily estimates sampling uncertainty, and the causal…

随机权重矩阵与回归变量下空间自回归模型的拟得分匹配估计AI 解读

With the rapid advancements in technology for data collection, the application of the spatial autoregressive (SAR) model has become increasingly prevalent in real-world analysis, particularly when dealing with large datasets. However, the commonly used quasi-maximum likelihood estimation (QMLE) for the SAR model is not computationally scalable to handle the data with a large size. In addition, when establishing the asymptotic properties of the parameter estimators of the SAR…

理解并避免 OLS 的“加权问题”：未建模的异质性与直接解决方案AI 解读

Researchers frequently estimate treatment effects by regressing outcomes (Y) on treatment (D) and covariates (X). Even without unobserved confounding, the coefficient on D yields a conditional-variance-weighted average of strata-wise effects, not the average treatment effect. Scholars have proposed characterizing the severity of these weights, evaluating resulting biases, or changing investigators' target estimand to the conditional-variance-weighted effect. We aim to…

内生群组中的社会互动AI 解读

This paper investigates social interactions in endogenous groups. We specify a two-sided many-to-one matching model, where individuals select groups based on preferences, while groups admit individuals based on qualifications until reaching capacities. Endogenous formation of groups leads to selection bias in peer effect estimation, which is complicated by equilibrium effects and alternative groups. We propose novel methods to simplify selection bias and develop a sieve OLS…

比例渐近下广义线性模型与双重稳健泛函的矩估计推断AI 解读

In this paper, we consider the estimation of regression coefficients and signal-to-noise (SNR) ratio in high-dimensional Generalized Linear Models (GLMs), and explore their implications in inferring popular estimands such as average treatment effects in high-dimensional observational studies. Under the ``proportional asymptotic'' regime and Gaussian covariates with known (population) covariance $Σ$, we derive Consistent and Asymptotically Normal (CAN) estimators of our…

细分层实验的新设计型方差估计量AI 解读

This paper considers the problem of design-based inference for the average treatment effect in finely stratified experiments. Here, by "design-based'' we mean that the only source of uncertainty stems from the randomness in treatment assignment; by "finely stratified'' we mean that units are stratified into groups of a fixed size according to baseline covariates and then, within each group, a fixed number of units are assigned uniformly at random to treatment and the…

聚合交集界与其他因果参数的去偏机器学习AI 解读

This paper proposes a novel framework of aggregated intersection of regression functions, where the target parameter is obtained by averaging the minimum (or maximum) of a collection of regression functions over the covariate space. Such quantities include the lower and upper bounds on distributional effects (Frechet-Hoeffding, Makarov) and the optimal welfare in the statistical treatment choice problem. The proposed estimator -- the envelope score estimator -- is shown to…

厘清随机临床试验中 Mantel–Haenszel 风险差估计量的作用AI 解读

The Mantel-Haenszel (MH) risk difference estimator, commonly used in randomized clinical trials for binary outcomes, calculates a weighted average of stratum-specific risk difference estimators. Traditionally, this method requires the stringent assumption that risk differences are homogeneous across strata, also known as the common (constant) risk difference assumption. In our article, we relax this assumption and adopt a modern perspective, viewing the MH risk difference…

内生性下删失回归模型的广义 M 估计AI 解读

We propose and study M-estimation to estimate the parameters in the censored regression model in the presence of endogeneity, i.e., the Tobit model. In the course of this study, we follow two-stage procedures: the first stage consists of applying control function procedures to address the issue of endogeneity using instrumental variables, and the second stage applies the M-estimation technique to estimate the unknown parameters involved in the model. The large sample…

Transformer 在上下文线性回归中处理内生性AI 解读

We explore the capability of transformers to address endogeneity in in-context linear regression. Our main finding is that transformers inherently possess a mechanism to handle endogeneity effectively using instrumental variables (IV). First, we demonstrate that the transformer architecture can emulate a gradient-based bi-level optimization procedure that converges to the widely used two-stage least squares $(\textsf{2SLS})$ solution at an exponential rate. Next, we propose…

使用估计倾向得分进行逆处理概率加权的调整 Nelson–Aalen 估计AI 解读

Inverse probability of treatment weighting (IPW) has been well applied in causal inference to estimate population-level estimands from observational studies. For time-to-event outcomes, the failure time distribution can be estimated by estimating the cumulative hazard in the presence of random right censoring. IPW can be performed by weighting the event counting process and at-risk process by the inverse treatment probability, resulting in an adjusted Nelson--Aalen estimator…

利用多来源数据高效估计目标总体平均处理效应AI 解读

We consider estimation of the target population average treatment effect (TATE) when outcome information is unavailable. Instead, we observe the outcome in multiple source populations and wish to combine the treatment effects therein to make inference on the TATE. In contrast to existing works that assume transportability on the conditional distribution of potential outcomes or conditional treatment-specific means, we work under a weaker form of effect transportability.…