每日简报

基于半参数累积概率模型的双重稳健分位数处理效应估计AI 解读

The causal inference literature has traditionally focused on estimating the mean of the potential outcome, whereas evaluating how a treatment affects the entire outcome distribution can provide additional information in biomedical research. Quantile treatment effect (QTE) captures such distributional differences, particularly when outcomes are skewed. However, existing approaches for estimating QTE make distributional assumptions about the outcome and are thus sensitive to…

随机潜在选择与潜在结果AI 解读

Applied econometricians typically model each individual as having fixed outcomes under treatment and control and, in instrumental-variables (IV) settings, fixed treatment decisions under each value of the instrument. This paper asks what changes when outcomes and treatment allocations or choices are stochastic at the individual level. In the model, each individual has a stable (but possibly stochastic) response type consisting of two objects: a treatment choice probability…

存在溢出效应的实验与准实验：基于设计的检验和推断AI 解读

Economic policies rarely affect only their direct targets. To study these spillovers, researchers summarize who else was treated with a simple exposure measure, such as the share of treated neighbors within a radius. But for many settings, economic theory provides little guidance on choosing the functional form (e.g., ring) of that measure or its parameters (e.g., radius). We show that the data can inform both choices. Correctly specified exposure measures imply…

何时及如何开展先导实验：两阶段实验的设计规则AI 解读

Experimenters often run pilots, but how much a small pilot should shape the main-wave design has no settled answer. This paper shows how noisy pilot evidence should guide treatment assignment probabilities in two-wave experiments. Two canonical rules mark the extremes. Balanced assignment guards against worst cases but ignores evidence that one arm is noisier. Feasible Neyman allocation adapts, but with a finite pilot it can overreact to noise, producing arbitrarily large…

成对数据回归模型的设定检验AI 解读

This paper develops omnibus specification tests for linear conditional-mean models with undirected dyadic data. We establish a uniform projection theorem that reduces the dyadic process to its latent first-order node projections under shared-node dependence. We then show that a raw first-order node-multiplier bootstrap is valid when this node component is nondegenerate but double-counts dyad-specific variation when dyads are independent. An exact covariance decomposition…

加权目标量的稳健推断AI 解读

Researchers often conduct inference on weighted estimands, defined as weighted averages of group-level effects. Example settings include event studies with cohort-level effects and experiments with site-level effects. Under heterogeneous effects, different weighting schemes yield estimands with distinct empirical and policy interpretations, leading to ambiguity and disagreement over the choice of weights. I establish bounds on differences between weighted estimands and…

合成双重差分中固定目标集合异质性的共享供体推断AI 解读

Empirical studies often estimate several synthetic-control or synthetic Difference-in-Differences effects from a common donor pool and then summarize their heterogeneity. Because the same donors enter several comparisons, the first-stage errors are jointly distributed and affect different targets differently. We characterize three regimes for linear fixed-set summaries: nonconcentration, root-rate donor exposure, and donornegligibility. We also show that removing…

存在无效工具变量时多重处理效应的识别与稳健推断AI 解读

The instrumental variable (IV) method is widely used to infer causal effects in observational studies with unmeasured confounding, but invalid instruments can compromise both population identification and finite-sample inference. This paper studies linear IV models with multiple endogenous treatments and possibly invalid instruments. Identification of multiple effects is more delicate than in the single-treatment setting because a single instrument no longer identifies a…

交错采用设计下多层面板线性泛函的估计AI 解读

We study the estimation of bilinear forms from noisy, partially observed multilayer data. The signal follows a Tucker2 model, with shared unit and time factors across tensor layers and slice-specific cores. The missingness pattern is structured and motivated by staggered adoption designs, which are common in causal inference and related applications. We first analyze the four-block missingness pattern, the basic building block for general staggered adoption, and propose a…

网络中的测量误差与同伴效应AI 解读

In many practical applications, only noisy proxies for the true regressors are available, which is commonly believed to induce an attenuation bias. In the linear-in-means model, however, estimated peer effects might be inflated, potentially leading to false positives. This paper shows that the asymptotic bias depends on the interplay between individual characteristics and network links and demonstrates how the network structure can facilitate identification without the need…

机器学习工具变量下的识别与推断AI 解读

Instrumental-variables estimation increasingly pools many or high-dimensional instruments into a single machine-learned first stage, with rich controls partialled out. The resulting estimand, the partialled-out IV coefficient built from any signal of the instruments, is a signal-weighted average of the heterogeneous effects, which gives an opaque first stage a precise structural meaning. The average is convex whenever a covariance-monotonicity condition holds, and we provide…

CausalSmith：以形式化验证为基础的自我改进因果推断研究代理框架AI 解读

Automating theoretical research requires generating candidate results and evaluating them reliably. Models keep getting better at the first, while the second remains hard. A common approach asks one large language model (LLM) to review what another produced, yet such reviewers are empirically unreliable: they may accept fabricated papers and catch the fabrication at close to chance rates~\citep{badscientist2025}. We present \textsc{CausalSmith}, a framework for automated…

保形化 Lee 推断：单调样本选择下个体治疗效应的预测集AI 解读

Treatment can change which outcomes are observed, so treated-selected and selected-control units need not represent the same latent population. This paper proposes conformalized Lee inference for counterfactual prediction under randomized treatment and monotone sample selection. The procedure uses treated-selected observations to fit and calibrate an arbitrary prediction rule and replaces the usual $(1-α)$ score quantile with the adjusted $(1-απ)$ quantile, where $π$ is the…

以太坊验证者的资金流向何处？一项谱分析AI 解读

Existing decentralization measures are almost entirely origination-side, quantifying concentration in who mines or validates blocks. We introduce a spectral methodology measuring concentration on the destination side instead: where value ultimately flows once it leaves a validator wallet. Modeling wallet-to-wallet transfers as a Markov chain, we compute near-real-time steady-state probabilities via the Perron--Frobenius theorem to identify long-run terminal recipients.…

最优连续处理效应的识别与估计AI 解读

Estimating continuous treatment effects is hard because average-derivative estimators rely on an ill-posed conditional-density score. Recent work makes a bounded outcome weight the primitive, characterizing a class of weighted average derivative effects without density estimation. In this paper, we develop the identification and estimation theory for the optimally efficient estimands of this class under homoskedasticity and heteroskedasticity. On identification, we show that…

处理是否有益？有序结局因果推断的贝叶斯方法AI 解读

Randomized experiments with ordinal outcomes are common across scientific applications, but conventional causal estimands such as the average treatment effect are difficult to interpret because ordinal categories lack meaningful numerical spacing. Two interpretable estimands, the probabilities that the treatment is beneficial and strictly beneficial, depend on the joint distribution of the potential outcomes and are therefore not identifiable from observed data. The sharp…

使用学习所得暴露映射开展存在干扰的因果推断AI 解读

Exposure mappings are often assumed to be known in causal spillover analyses. In environmental settings, however, they are typically induced by transport processes that are not directly observed and must instead be learned from pollution data. We study how uncertainty in learned transport processes propagates into exposure mappings and downstream spillover inference under interference. We compare mechanistic transport models with modern operator-learning approaches,…

拓扑因果数据分析的数学框架AI 解读

Many modern outcomes, including images, point clouds, networks, and spatial fields, are structured objects for which \(Y^1-Y^0\) may be undefined or scientifically inadequate. We introduce \emph{Topological Causal Data Analysis} (TCDA), a framework separating the observation space, causal-model class, topological representation, and causal query. Topology does not define interventions; it supplies stable, shape-sensitive summaries after causal assumptions have been…

部分干扰与增量倾向得分政策下的高效双重差分估计AI 解读

This paper develops efficient difference-in-differences (DID) estimation under partial interference with a cluster incremental propensity score (CIPS) policy. We define direct and spillover average treatment effects on the treated, establish their identification, and derive their efficient influence functions, from which we construct a cross-fitted estimator. Simulations evaluate its finite-sample performance. An application to China's New Rural Pension Scheme recovers the…

组成数据的合成控制AI 解读

When applying synthetic control to compositional outcomes (budget, vote shares), researchers commonly minimize Euclidean distances between raw shares. I propose constructing synthetic controls in Aitchison geometry, using centered log-ratio coordinates to select donor weights and form counterfactuals. This approach respects proportional comparisons and is exactly invariant to common multiplicative changes in relative odds. Under a multinomial-choice model, its weights…

快速偏分位数回归AI 解读

Partial least squares (PLS) is a dimensionality reduction technique used as an alternative to ordinary least squares (OLS) in situations where the data is colinear or high dimensional. Both PLS and OLS provide mean based estimates, which are extremely sensitive to the presence of outliers or heavy tailed distributions. In contrast, quantile regression is an alternative to OLS that computes robust quantile based estimates. In this work, the multivariate PLS is extended to the…

各期处理均连续分布时的双重差分估计量AI 解读

When studying the effects of taxes, tariffs, or prices using panel data, the treatment is often continuously distributed in every period. We develop difference-in-differences (DID) estimators for such settings. We partition units into switchers, whose treatment changes between consecutive periods, and stayers, whose treatment remains constant. Under a parallel-trends assumption, we show that the slopes of switchers' potential outcomes with respect to the treatment are…

针对排除限制与外生性违背的广义证伪适应集AI 解读

The falsification adaptive set (FAS) as proposed by Masten and Poirier (2021) provides an identified set for a treatment effect when the baseline model is falsified, assuming invalid instruments violate exclusion only. We show that whether an invalid instrument is a confounder or collider has important consequences: incorrect treatment can cause the FAS to exclude the true parameter. We derive pattern-specific falsification adaptive sets for each combination of violations…

平行趋势假设下的结构嵌套均值模型AI 解读

We link and extend two approaches to estimating time-varying treatment effects on repeated continuous outcomes--time-varying Difference in Differences (DiD; see Roth et al. (2023) and Chaisemartin et al. (2023) for reviews) and Structural Nested Mean Models (SNMMs; see Vansteelandt and Joffe (2014) for a review). In particular, we show that SNMMs, previously known to be nonparametrically identified under a no unobserved confounding assumption, are also identified under a…