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异质缺失数据下的最优传输AI 解读

We consider the problem of solving the optimal transport problem between two empirical distributions with missing values. Our main assumption is that the data is missing completely at random (MCAR), but we allow for heterogeneous missingness probabilities across features and across the two distributions. As a first contribution, we show that the Wasserstein distance between empirical Gaussian distributions and linear Monge maps between arbitrary distributions can be debiased…

局部投影还是 VAR？宏观经济学者入门指南AI 解读

What should applied macroeconomists know about local projection (LP) and vector autoregression (VAR) impulse response estimators? The two methods share the same estimand, but in finite samples lie on opposite ends of a bias-variance trade-off. While the low bias of LPs comes at a quite steep variance cost, this cost must be paid to achieve robust uncertainty assessments. Hence, when the goal is to convey what can be learned about dynamic causal effects from the data, VARs…

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…

纵向离散结局研究中暴露测量误差的校正AI 解读

Environmental epidemiologists are often interested in estimating the effect of time-varying functions of the exposure history on health outcomes. However, the individual exposure measurements that constitute the history upon which an exposure history function is constructed are usually subject to measurement errors. To obtain unbiased estimates of the effects of such mismeasured functions in longitudinal studies with discrete outcomes, a method applicable to the main…

重复横截面与期内观测的分布动态分析AI 解读

This paper introduces a novel approach to investigate the dynamics of state distributions, which accommodate both cross-sectional distributions of repeated panels and intra-period distributions of a time series observed at high frequency. In our approach, densities of the state distributions are regarded as functional elements in a Hilbert space, and are assumed to follow a functional autoregressive model. We propose an estimator for the autoregressive operator, establish…

多时期分布型双重差分模型AI 解读

Researchers are often interested in evaluating the impact of a policy on the entire (or specific parts of the) distribution of the outcome of interest. In this paper, I provide a method to recover the whole distribution of the untreated potential outcome for the treated group in non-experimental settings with staggered treatment adoption by generalizing the existing quantile treatment effects on the treated (QTT) estimator proposed by Callaway and Li (2019). Besides the QTT,…

矩阵值自回归模型的估计方法AI 解读

This article proposes novel estimation methods for the Matrix Autoregressive (MAR) model, specifically adaptations of the Yule-Walker equations and Burg's method, addressing limitations in existing techniques. The MAR model, by maintaining a matrix structure and requiring significantly fewer parameters than vector autoregressive (VAR) models, offers a parsimonious, yet effective, alternative for high-dimensional time series. Empirical results demonstrate that MAR models…

基于高维 Potts 模型与结构信息的蛋白质突变适应度建模与预测AI 解读

Quantifying the effects of amino acid mutations in proteins presents a significant challenge due to the vast combinations of residue sites and amino acid types, making experimental approaches costly and time-consuming. The Potts model has been used to address this challenge, with parameters capturing evolutionary dependency between residue sites within a protein family. However, existing methods often use the mean-field approximation to reduce computational demands, which…

模型误设下的动态决策AI 解读

In this study, I investigate the dynamic decision problem with a finite parameter space when the functional form of conditional expected rewards is misspecified. Traditional algorithms, such as Thompson Sampling, guarantee neither an $O(e^{-T})$ rate of posterior parameter concentration nor an $O(T^{-1})$ rate of average regret. However, under mild conditions, we can still achieve an exponential convergence rate of the parameter to a pseudo truth set, an extension of the…

受控马尔可夫链转移密度的自适应估计AI 解读

Estimating the transition dynamics of controlled Markov chains is crucial in fields such as time series analysis, reinforcement learning, and system exploration. Traditional non-parametric density estimation methods often assume independent samples and require oracle knowledge of smoothness parameters like the Hölder continuity coefficient. These assumptions are unrealistic in controlled Markovian settings, especially when the controls are non-Markovian, since such…

效率分析中的后双重 LASSOAI 解读

Big data and machine learning methods have become commonplace across economic milieus. One area that has not seen as much attention to these important topics yet is efficiency analysis. We show how the availability of big (wide) data can actually make detection of inefficiency more challenging. We then show how machine learning methods can be leveraged to adequately estimate the primitives of the frontier itself as well as inefficiency using the `post double LASSO' by…

利用观察数据学习最优动态处理规则AI 解读

Public policies and medical interventions often involve dynamic treatment assignments, in which individuals receive a sequence of interventions over multiple stages. We study the statistical learning of optimal dynamic treatment regimes (DTRs) that determine the optimal treatment assignment for each individual at each stage based on their evolving history. We propose a novel, doubly robust, classification-based method for learning the optimal DTR from observational data…

非线性元学习可保证更快收敛速率AI 解读

Many recent theoretical works on \emph{meta-learning} aim to achieve guarantees in leveraging similar representational structures from related tasks towards simplifying a target task. The main aim of theoretical guarantees on the subject is to establish the extent to which convergence rates -- in learning a common representation -- \emph{may scale with the number $N$ of tasks} (as well as the number of samples per task). First steps in this setting demonstrate this property…

非平稳时间序列中稳定分布与 Hurst 指数的自适应移动矩估计AI 解读

Nonstationarity of real-life time series requires model adaptation. In classical approaches like ARMA-ARCH there is assumed some arbitrarily chosen dependence type. To avoid their bias, we will focus on novel more agnostic approach: moving estimator, which estimates parameters separately for every time $t$: optimizing $F_t=\sum_{τ<t} (1-η)^{t-τ} \ln(ρ_θ(x_τ))$ local log-likelihood with exponentially weakening weights of the old values. In practice such moving estimates can…

噪声观测下随机演化方程推断的信息界AI 解读

We consider statistics for stochastic evolution equations in Hilbert space with emphasis on stochastic partial differential equations (SPDEs). We observe a solution process under additional measurement errors and want to estimate a real or functional parameter in the drift. Main targets of estimation are the diffusivity, transport or source coefficient in a parabolic SPDE. By bounding the Hellinger distance between observation laws under different parameters we derive lower…

气候变化检测与归因中的正则化指纹法：线性最优权重矩阵AI 解读

Climate change detection and attribution play a central role in establishing the causal influence of human activities on global warming. The dominant framework, optimal fingerprinting, is a linear errors-in-variables model in which each covariate is subject to measurement error with covariance proportional to that of the regression error. The reliability of such analyses depends critically on accurate inference of the regression coefficients. The optimal weight matrix for…

有限样本推断中完全由异常值驱动的线性回归系数显著性AI 解读

In this paper, we investigate the impact of outliers on the statistical significance of coefficients in linear regression. We demonstrate, through numerical simulation using R, that a single outlier can cause an otherwise insignificant coefficient to appear statistically significant. We compare this with robust Huber regression, which reduces the effects of outliers. Afterwards, we approximate the influence of a single outlier on estimated regression coefficients and discuss…

在 2SLS 第一阶段使用机器学习：来自偏差分解与模拟的实践指导AI 解读

Machine learning (ML) primarily evolved to solve "prediction problems." The first stage of two-stage least squares (2SLS) is a prediction problem, suggesting potential gains from ML first-stage assistance. However, little guidance exists on when ML helps 2SLS$\unicode{x2014}$or when it hurts. We investigate the implications of inserting ML into 2SLS, decomposing the bias into three informative components. Mechanically, ML-in-2SLS procedures face issues common to prediction…

含潜在混杂的汇总因果图中微观总效应的可识别性：前门准则扩展AI 解读

Conducting experiments to estimate total effects can be challenging due to cost, ethical concerns, or practical limitations. As an alternative, researchers often rely on causal graphs to determine whether these effects can be identified from observational data. Identifying total effects in fully specified causal graphs has received considerable attention, with Pearl's front-door criterion enabling the identification of total effects in the presence of latent confounding even…

使用控制函数的准贝叶斯估计与推断AI 解读

This paper introduces a quasi-Bayesian method that integrates frequentist nonparametric estimation with Bayesian inference in a two-stage process. Applied to an endogenous discrete choice model, the approach first uses kernel or sieve estimators to estimate the control function nonparametrically, followed by Bayesian methods to estimate the structural parameters. This combination leverages the advantages of both frequentist tractability for nonparametric estimation and…

总体层面因果效应度量可迁移性的统一框架AI 解读

Generalization methods offer a powerful solution to one of the key drawbacks of randomized controlled trials (RCTs): their limited representativeness. By enabling the transport of treatment effect estimates to target populations subject to distributional shifts, these methods are increasingly recognized as the future of meta-analysis, the current gold standard in evidence-based medicine. Yet most existing approaches focus on the risk difference, overlooking the diverse range…

基于极大似然估计的带标记 Hawkes 过程检验方法AI 解读

The Hawkes model is a past-dependent point process, widely used in various fields for modeling temporal clustering of events. Extending this framework, the multidimensional marked Hawkes process incorporates multiple interacting event types and additional marks, enhancing its capability to model complex dependencies in multivariate time series data. However, increasing the complexity of the model also increases the computational cost of the associated estimation methods and…

稳健因果估计的校准策略：倾向得分估计量的理论与实证分析AI 解读

The partitioning of data for estimation and calibration critically impacts the performance of propensity score based estimators like inverse probability weighting (IPW) and double/debiased machine learning (DML) frameworks. We extend recent advances in calibration techniques for propensity score estimation, improving the robustness of propensity scores in challenging settings such as limited overlap, small sample sizes, or unbalanced data. Our contributions are twofold:…

通过分区估计处理伤害率AI 解读

In causal inference with binary outcomes, there is a growing interest in estimation of treatment harm rate (THR), which is a measure of treatment risk and reveals treatment effect heterogeneity in a subpopulation. The THR is generally non-identifiable even for randomized controlled trials (RCTs), and existing works focus primarily on the estimation of the THR under either untestable identification or ambiguous model assumptions. We develop a class of partitioning-based…