每日简报

有界异质性下的处理效应估计AI 解读

Specifications that impose constant treatment effects are common and can be biased under heterogeneity, whereas fully flexible alternatives can be imprecise or infeasible. Under a bound on the target-weighted variance of treatment effects, this paper proposes a generalized ridge estimator, $\texttt{regulaTE}$, that yields heterogeneity-aware confidence intervals (CIs). The ridge penalty is chosen to minimize the heterogeneity-aware CI length in a homoskedastic Gaussian…

因果机器学习的敏感性分析：Booking.com 应用示例AI 解读

Causal Machine Learning has emerged as a powerful tool for flexibly estimating causal effects from observational data in both industry and academia. However, causal inference from observational data relies on untestable assumptions about the data-generating process, such as the absence of unobserved confounders. When these assumptions are violated, causal effect estimates may become biased, undermining the validity of research findings. In these contexts, sensitivity…

状态空间模型的 MCMC 方法AI 解读

A state-space model is a time-series model that has an unobserved latent process from which we take noisy measurements over time. The observations are conditionally independent given the latent process and the latent process itself is Markovian. These properties lead to simplifications for the conditional distribution of the latent process given the parameters and the observations. This chapter looks at how we can leverage the properties of state-space models to construct…

图形连续 Lyapunov 模型的结构可识别性AI 解读

We prove two characterizations of model equivalence of acyclic graphical continuous Lyapunov models (GCLMs) with uncorrelated noise. The first result shows that two graphs are model equivalent if and only if they have the same skeleton and equivalent induced 4-node subgraphs. We also give a transformational characterization via structured edge reversals. The two theorems are Lyapunov analogues of celebrated results for Bayesian networks by Verma and Pearl, and Chickering,…

高维时间序列的稀疏组因子分析AI 解读

Factor analysis is a widely used technique for dimension reduction in high-dimensional data. However, a key challenge in factor models lies in the interpretability of the latent factors. One intuitive way to interpret these factors is through their associated loadings. Liu and Wang proposed a novel framework that redefines factor models with sparse loadings to enhance interpretability. In many high-dimensional time series applications, variables exhibit natural group…

有序时间序列的二元一阶离散自回归模型AI 解读

We present a bivariate vector valued discrete autoregressive model of order $1$ (BDAR($1$)) for discrete time series. The BDAR($1$) model assumes that each time series follows its own univariate DAR($1$) model with dependent random mechanisms that determine from which component the current status occurs and dependent innovations. The joint distribution of the random mechanisms which are expressed by Bernoulli vectors are proposed to be defined through copulas. The same holds…

关于条件异方差时间序列拟极大似然估计的随机递推方法的一则注记AI 解读

Bougerol (1993) and Straumann and Mikosch (2006) gave conditions under which there exists a unique stationary and ergodic solution to the stochastic difference equation $Y_t \overset{a.s.}{=} Φ_t (Y_{t-1}), t \in \mathbb{Z}$ where $(Φ_t)_{t \in \mathbb{Z}}$ is a sequence of stationary and ergodic random Lipschitz continuous functions from $(Y,|| \cdot ||)$ to $(Y,|| \cdot ||)$ where $(Y,|| \cdot ||)$ is a complete subspace of a real or complex separable Banach space. In the…

加密货币网络数据中的张量时间序列变点检测AI 解读

Financial fraud has been growing exponentially in recent years. The rise of cryptocurrencies as an investment asset has simultaneously seen a parallel growth in cryptocurrency scams. To detect possible cryptocurrency fraud, and in particular market manipulation, previous research focused on the detection of changes in the network of trades; however, market manipulators are now trading across multiple cryptocurrency platforms, making their detection more difficult. Hence, it…

成交价格截断下的拍卖识别AI 解读

I establish nonparametric identification results in first- and second-price auctions when transaction prices are truncated by a binding reserve price under a range of information structures. When the number of potential bidders is fixed and known across all auctions, if only the transaction price is observed, the bidders' private-value distribution is identified in second-price auctions but not in first-price auctions. Identification in first-price auctions can be achieved…

基于预测综合的贝叶斯投资组合优化AI 解读

Portfolio optimization is a critical task in investment. Most existing portfolio optimization methods require information on the distribution of returns of the assets that make up the portfolio. However, such distribution information is usually unknown to investors. Various methods have been proposed to estimate distribution information, but their accuracy greatly depends on the uncertainty of the financial markets. Due to this uncertainty, a model that could well predict…

生存结局聚类随机试验的目标参数与双重稳健估计AI 解读

Cluster-randomized trials (CRTs) are experimental designs where groups or clusters of participants, rather than the individual participants themselves, are randomized to intervention groups. Analyzing CRT requires distinguishing between treatment effects at the cluster level and the individual level, which requires a clear definition of the estimands under the potential outcomes framework. For analyzing survival outcomes, it is common to assess the treatment effect by…

文本因果推断的设计式方案：语言模型会不会过大？AI 解读

Many social science questions ask how linguistic properties causally affect an audience's attitudes and behaviors. Because text properties are often interlinked (e.g., angry reviews use profane language), we must control for possible latent confounding to isolate causal effects. Recent literature proposes adapting large language models (LLMs) to learn latent representations of text that successfully predict both treatment and the outcome. However, because the treatment is a…

利用 Riesz 表示开展双重差分处理效应敏感性分析AI 解读

Difference-in-differences (DiD) is one of the most popular approaches for empirical research in economics, political science, and beyond. Identification in these models is based on the conditional parallel trends assumption: In the absence of treatment, the average outcome of the treated and untreated group are assumed to evolve in parallel over time, conditional on pre-treatment covariates. We introduce a novel approach to sensitivity analysis for DiD models that assesses…

断点回归的边界估计：基于成绩与经济需要的助学项目证据AI 解读

In the conventional regression-discontinuity (RD) design, the probability that units receive a treatment changes discontinuously as a function of one covariate exceeding a threshold or cutoff point. This paper studies an extended RD design where assignment rules simultaneously involve two or more continuous covariates. We show that assignment rules with more than one variable allow the estimation of a more comprehensive set of treatment effects, relaxing in a research-driven…

依赖且异质的重尾数据中的广义泰勒定律AI 解读

Taylor's law, also known as fluctuation scaling in physics and the power-law variance function in statistics, is an empirical pattern widely observed across fields including ecology, physics, finance, and epidemiology. It states that the variance of a sample scales as a power function of the mean of the sample. We study generalizations of Taylor's law in the context of heavy-tailed distributions with infinite mean and variance. We establish the probabilistic limit and…

随机实验异质性处理效应估计的去噪IPW-Lasso方法AI 解读

This paper proposes a new method for estimating conditional average treatment effects (CATE) in randomized experiments. We adopt inverse probability weighting (IPW) for identification; however, IPW-transformed outcomes are known to be noisy, even when true propensity scores are used. To address this issue, we introduce a noise reduction procedure and estimate a linear CATE model using Lasso, achieving both accuracy and interpretability. We theoretically show that denoising…

分类结果的样本选择偏差校正AI 解读

In this paper, I propose a method for correcting sample selection bias when the outcome of interest is categorical, such as occupational choice, health status, or field of study. Classical approaches to sample selection rely on strong parametric distributional assumptions, which may be restrictive in practice. I develop a local representation that decomposes each joint probability into marginal probabilities and a category-specific association parameter that captures how…

货币冲击对宏观经济星号变量的影响：SMUC-IV 框架AI 解读

This paper proposes a structural multivariate unobserved components model with external instrument (SMUC-IV) to investigate the effects of monetary policy shocks on key U.S. macroeconomic "stars"-namely, the level of potential output, the growth rate of potential output, trend inflation, and the neutral interest rate. A key feature of our approach is the use of an external instrument to identify monetary policy shocks within the multivariate unobserved components modeling…

无先验的 Blackwell 信息比较AI 解读

This paper develops a prior-free model of data-driven decision making in which the decision maker observes the entire distribution of signals generated by a known experiment under an unknown distribution of the state variable and evaluates actions according to their worst-case payoff over the set of state distributions consistent with that observation. We show how our model applies to partial identification in econometrics and propose a ranking of experiments in which E is…

因子分析中的最大软惩罚似然估计AI 解读

Estimation in exploratory factor analysis often yields estimates on the boundary of the parameter space. Such occurrences, known as Heywood cases, are characterised by non-positive variance estimates and can cause issues in numerical optimisation procedures or convergence failures, which, in turn, can lead to misleading inferences, particularly regarding factor scores and model selection. We derive sufficient conditions on the model and a penalty to the log-likelihood…

拥抱离散搜索：因果结构学习的一种可行方法AI 解读

We present FLOP (Fast Learning of Order and Parents), a score-based causal discovery algorithm for linear models. It pairs fast parent selection with iterative Cholesky-based score updates, cutting run-times over prior algorithms. This makes it feasible to fully embrace discrete search, enabling iterated local search with principled order initialization to find graphs with scores at or close to the global optimum. The resulting structures are highly accurate across…

ResCP：用于时间序列预测的储备池共形预测AI 解读

Conformal prediction offers a powerful framework for building distribution-free prediction intervals for exchangeable data. Existing methods that extend conformal prediction to sequential data rely on fitting a relatively complex model to capture temporal dependencies. However, these methods can fail if the sample size is small and often require expensive retraining when the underlying data distribution changes. To overcome these limitations, we propose Reservoir Conformal…

超越预言机性质：近单位根解释变量下协整回归的自适应LASSOAI 解读

This paper derives new asymptotic results for the adaptive LASSO estimator in cointegrating regressions, allowing for uncertainty about whether the regressors are exact unit root processes. We study model selection probabilities, estimator consistency, and limiting distributions under standard and moving-parameter asymptotics. We further derive uniform convergence rates and the fastest local-to-zero rates detectable by the estimator under conservative and consistent tuning.…

网络依赖时间序列的频域分析：全球银行关联应用AI 解读

Financial spillovers in interconnected systems, such as global banking networks, require tools that capture temporal and frequency dynamics, while incorporating the underlying network topology. While current network time series models are developed in the time-domain, frequency-domain approaches, which reveal how cross-nodal dependencies vary across different cycles, remain under-explored. This paper develops a spectral analysis framework that accommodates flexible forms of…