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因果发现应拥抱群体智慧AI 解读

This paper argues for recognizing an emerging paradigm of causal learning by wisdom of the crowd. Recent developments in government, industry, and research point to the rise of decentralized and crowd-based approaches within causal modeling, where causal knowledge distributed across many contributors can be systematically elicited and integrated with causal learning workflows. In this paradigm, causal learning becomes a distributed decision-making problem: each participant…

可迁移分位数处理效应的嵌套敏感性包络AI 解读

We study target-population quantile treatment effects when a source study may have unmeasured treatment confounding and may not transport to a target population after conditioning on observed covariates. The observed data consist of a source sample with treatment, outcome and covariates, and a target sample with covariates only. We impose two marginal sensitivity restrictions: an odds-ratio bound \(\Gam\) for source treatment assignment and a conditional likelihood-ratio…

从异质性效应中提取机制：中介分析的识别策略AI 解读

Understanding causal mechanisms is crucial for explaining and generalizing empirical phenomena. Causal mediation analysis offers statistical techniques to quantify the mediation effects. Although numerous methods have been developed for causal inference more broadly, the methodological toolkit for causal mediation analysis remains limited. Current methods often require multiple ignorability assumptions or sophisticated research designs. In this paper, we introduce an…

用函数型回归量化随时间变化的体力活动干预效应AI 解读

Physical activity (PA) intervention studies often collect repeated intensity measurements over long observation periods. Quantifying the variation in intervention effects over the study period is critical to evaluating and improving intervention strategies, yet many analyses reduce PA data into scalar summary measures, resulting in limited insights. We propose a functional regression framework, which captures time-varying intervention effects by modeling the entire PA…

检验双重差分平行趋势违背时的有效推断AI 解读

The difference-in-differences (DID) research design is a key identification strategy which allows researchers to estimate causal effects under the parallel trends assumption. While the parallel trends assumption is counterfactual and cannot be tested directly, researchers often examine pre-treatment periods to check whether the time trends are parallel before treatment is administered. A recent literature has shown that existing preliminary tests have adverse effects on…

只拟合一次 CATE：无需样本分割的模型辅助随机化检验AI 解读

Randomization tests and flexible treatment-effect models offer complementary strengths for analyzing data from randomized panel experiments: the former provide valid inference under the known assignment mechanism, while the latter can capture complex patterns of effect heterogeneity. We develop model-assisted randomization tests that combine these strengths without sample splitting. The key idea is to estimate an unsigned version of the conditional average treatment effect…

网络依赖与干扰下双重差分的高效非参数估计AI 解读

Differences-in-differences (DiD) is a causal inference method for observational longitudinal data that assumes parallel expected potential outcome trajectories between treatment groups under the counterfactual scenario where all units receive a specific treatment. In this paper DiD is extended to allow for: (i) non-identically distributed treatment effects and exposure probabilities; (ii) interference, where treatment of one unit can affect outcomes in neighboring units; and…

含因子结构的噪声高频观测下非凸高维时变系数估计AI 解读

In this paper, we propose a novel high-dimensional time-varying coefficient estimator for noisy high-frequency observations with a factor structure. In high-frequency finance, we often observe that noises dominate the signal of underlying true processes and that covariates exhibit a factor structure due to their strong dependence. Thus, we cannot apply usual regression procedures to analyze high-frequency observations. To handle the noises, we first employ a smoothing method…

启发式评分估计方法的适用充分条件AI 解读

A series of papers has introduced the Heuristic Rating Estimation method, which evaluates a set of alternatives based on pairwise comparisons and the weights of reference alternatives. We formulate the conditions under which the HRE method can be applied correctly. The research considers both arithmetic and geometric algorithms for complete and incomplete pairwise comparison methods. The illustrative examples show that the estimations of inconsistency in the arithmetic…

现代异质性稳健双重差分估计量的基于设计方差估计AI 解读

Modern heterogeneity-robust difference-in-differences estimators derive their asymptotic properties under iid, cluster, or fixed-design frameworks that abstract from complex survey sampling, yet practitioners routinely apply them to nationally representative surveys with stratified cluster designs. We show that, under standard regularity conditions, the influence functions of each smooth IF-based or regression-based modern DiD estimator satisfy Binder's (1983) smoothness…

通过有限观测信息汇总界定处理效应AI 解读

We provide novel bounds on average treatment effects (on the treated) that are valid under an unconfoundedness assumption. Our bounds are designed to be robust in challenging situations, for example, when the conditioning variables take on a large number of different values in the observed sample, or when the overlap condition is violated. This robustness is achieved by only using limited "pooling" of information across observations. Namely, the bounds are constructed as…

基于函数型分位数自相关的函数型时间序列白噪声检验AI 解读

We introduce a novel class of nonlinear tests for serial dependence in functional time series, grounded in the functional quantile autocorrelation framework. Unlike traditional approaches based on the classical autocovariance kernel, the functional quantile autocorrelation framework leverages quantile-based excursion sets to robustly capture temporal dependence within infinite-dimensional functional data, accommodating potential outliers and complex nonlinear dependencies.…

利用夜间灯光即时预测意大利市镇收入：深度学习方法AI 解读

This paper assesses whether NASA Black Marble nightlight intensity can serve as an early indicator of annual taxable income at the Italian municipal level, where official data are released with a 12--18 month lag. Using a panel of 7{,}631 municipalities over 2012--2021, we compare four recurrent neural network architectures (LSTM, BiLSTM, GRU, Transformer) against six benchmarks: simple persistence, panel fixed effects, autoregressive distributed lag, and two spatial…

是否存在人工智能泡沫？亢奋期的稳健起止时间识别AI 解读

The recent surge in valuations among AI related firms has renewed concerns that markets may be entering a new phase of speculative exuberance, especially in the technology and semiconductor sectors at the center of the AI investment wave. This paper develops a practical econometric framework for detecting, date-stamping, and drawing inference on the origination and collapse of bubble episodes when prices evolve under persistent, time-varying volatility. Standard bubble tests…

非参数经验贝叶斯置信区间AI 解读

Empirical Bayes methods can improve inference on unobservable individual effects by borrowing strength across units. This paper proposes nonparametric empirical Bayes confidence intervals (NP-EBCIs) for unobservable individual effects in a normal means model. The oracle intervals are constructed from posterior quantiles under a point-identified, fully nonparametric prior; feasible intervals replace these quantiles with nonparametric estimates. The NP-EBCIs are asymptotically…

面向对象空间统计框架下的人类活动空间估计方法AI 解读

Human activity spaces are shaped by individual mobility and the built environment, motivating statistical methods that integrate GPS observations with GIS representations of places and routes. We propose a novel methodology to estimate activity spaces in built environments from GPS data within the Object Oriented Spatial Statistics framework. We characterize daily mobility through the distribution of time across spatial polygons and road segments, aiming to capture…

Sinkhorn 处理效应：一种因果最优传输度量AI 解读

We introduce the Sinkhorn treatment effect, an entropic optimal transport measure of divergence between counterfactual distributions. Unlike classical quantities such as the average treatment effect, this measure captures differences across entire distributions. We analyze this divergence as a statistical functional and show it can be written as a smooth transformation of counterfactual mean embeddings with an appropriate kernel. This characterization allows us to establish…

局部平稳与弱依赖下的滚动起点保形预测AI 解读

We propose and analyse rolling-origin conformal prediction for time-series forecasting. The method calibrates the conformal quantile against the $m$ most recent pseudo-out-of-sample forecast errors, adapting to serial dependence, volatility clustering, and distributional drift that invalidate classical conformal guarantees. Under Hölder-$β$ local stationarity and $α$-mixing, we establish a four-term coverage-error decomposition and derive the optimal calibration window $m^{\star} \asymp T^{2β/(2β+1)}$…

有限总体识别与基于设计的敏感性分析AI 解读

We develop a new approach for quantifying uncertainty in finite populations, by using design distributions to calibrate sensitivity parameters in finite population identified sets. This yields uncertainty intervals that can be interpreted as identified sets, robust Bayesian credible sets, or uniform frequentist design-based confidence sets. We focus on quantifying uncertainty about the average treatment effect, where our approach (1) yields design-based confidence intervals…

“氛围计量经济学”与分析契约AI 解读

"Vibe coding" and "vibe analytics" have been framed as a democratization of technical capability. This paper argues that AI-assisted methodology more broadly, or what I call "vibe methodology," also democratizes the failure modes specific to each domain. When AI assists with methods whose validity depends on assumptions that cannot be verified from the output alone (a class I call "vibe inference"), the failure surface is structurally different: the output does not reliably…

结合秩统计量的个体处理效应分布随机化检验AI 解读

What proportion of treated units actually benefited from an experimental intervention? What is the median or the largest individual treatment effect? This paper develops methods for answering such questions about the distribution of individual causal effects in randomized experiments. Existing approaches require the analyst to select a rank-based test statistic before observing the data. A poor choice can substantially reduce power, while searching over multiple test…

结果缺失下的多环境不变性学习AI 解读

Learning models that can handle distribution shifts is a key challenge in domain generalization. Invariance learning, an approach that focuses on identifying features invariant across environments, improves model generalization by capturing stable relationships, which may represent causal effects when the data distribution is encoded within a structural equation model (SEM) and satisfies modularity conditions. This has led to a growing body of work that builds on invariance…

基于证据先验的因果估计量贝叶斯敏感性分析AI 解读

Causal inference, especially in observational studies, relies on untestable assumptions about the true data-generating process. Sensitivity analysis helps us determine how robust our conclusions are when we alter these underlying assumptions. Existing frameworks for sensitivity analysis are concerned with worst-case changes in assumptions. In this work, we argue that using such pessimistic criteria can often become uninformative or lead to conclusions contradicting our prior…

处理效应异质性的变量重要性推断：Shapley 值及其拓展AI 解读

We provide an inferential framework to assess variable importance for heterogeneous treatment effects. This assessment is especially useful in high-risk domains such as medicine, where decision makers hesitate to rely on black-box treatment recommendation algorithms. The variable importance measures we consider are local in that they may differ across individuals, while the inference is global in that it tests whether a given variable is important for any individual. Our…