每日简报

因果推断中多类处理效应的统一估计方法AI 解读

In this paper, we introduce a unified estimator to analyze various treatment effects in causal inference, including but not limited to the average treatment effect (ATE) and the quantile treatment effect (QTE). The proposed estimator is developed under the statistical functional and cumulative distribution function structure, which leads to a flexible and robust estimator and covers some frequent treatment effects. In addition, our approach also takes variable selection into…

将元分析整合到特定研究中以估计条件平均处理效应：InMASSAI 解读

Randomized controlled trials are the standard method for estimating causal effects, ensuring sufficient statistical power and confidence through adequate sample sizes. However, achieving such sample sizes is often challenging. This study proposes a novel method for estimating the average treatment effect (ATE) in a target population by integrating and reconstructing information from previous trials using only summary statistics of outcomes and covariates through…

固定时期数线性因子模型中处理效应的识别与估计AI 解读

This paper provides a new approach for identifying and estimating the Average Treatment Effect on the Treated under a linear factor model that allows for multiple time-varying unobservables. Unlike the majority of the literature on treatment effects in linear factor models, our approach does not require the number of pre-treatment periods to go to infinity to obtain a valid estimator. Our identification approach employs a certain nonlinear transformations of the time…

两层嵌套 Logit 与序贯 LogitAI 解读

This technical note provides comprehensive derivations of fundamental equations in two-level nested and sequential logit models for analyzing hierarchical choice structures. We present derivations of the Berry (1994) inversion formula, nested inclusive values computation, and multi-level market share equations, complementing existing literature. While conceptually distinct, nested and sequential logit models share mathematical similarities and, under specific distributional…

具有因果保证的高效小型智能体评估计算理论AI 解读

In order to reduce the cost of experimental evaluation for agents, we introduce a computational theory of evaluation for mini agents: build evaluation model to accelerate the evaluation procedures. We prove upper bounds of generalized error and generalized causal effect error of given evaluation models for infinite agents. We also prove efficiency, and consistency to estimated causal effect from deployed agents to evaluation metric by prediction. To learn evaluation models,…

高维工具变量与控制变量下的处理效应推断AI 解读

Obtaining valid treatment effect inference remains a challenging problem when dealing with numerous instruments and non-sparse control variables. In this paper, we propose a novel ridge regularization-based instrumental variables method for estimation and inference in the presence of both high-dimensional instrumental variables and high-dimensional control variables. These methods are applicable both with and without sparsity assumptions. To remove the estimation bias, we…

重新理解需求估计AI 解读

This paper clarifies how and why structural demand models (Berry and Haile, 2014, 2024) predict unit-level counterfactual outcomes. We do so by casting structural assumptions equivalently as restrictions on the joint distribution of potential outcomes. Our reformulation highlights a counterfactual homogeneity assumption underlying structural demand models: The relationship between counterfactual outcomes is assumed to be identical across markets. This assumption is strong,…

多重假设检验后的效应量推断AI 解读

Significant treatment effects are often emphasized when interpreting and summarizing empirical findings in studies that estimate multiple, possibly many, treatment effects. Under this kind of selective reporting, conventional treatment effect estimates may be biased and their corresponding confidence intervals may undercover the true effect sizes. We propose new estimators and confidence intervals that provide valid inferences on the effect sizes of the significant effects…

含潜变量的单一或重叠数据集中的分层背景知识与约束式因果发现AI 解读

In this paper we consider the use of tiered background knowledge within constraint based causal discovery. Our focus is on settings relaxing causal sufficiency, i.e. allowing for latent variables which may arise because relevant information could not be measured at all, or not jointly, as in the case of multiple overlapping datasets. We first present novel insights into the properties of the 'tiered FCI' (tFCI) algorithm. Building on this, we introduce a new extension of the…

存在选择偏差与混杂时的因果效应回归估计AI 解读

We consider the problem of estimating the expected causal effect $E[Y|do(X)]$ for a target variable $Y$ when treatment $X$ is set by intervention, focusing on continuous random variables. In settings without selection bias or confounding, $E[Y|do(X)] = E[Y|X]$, which can be estimated using standard regression methods. However, regression fails when systematic missingness induced by selection bias, or confounding distorts the data. Proxy variables unaffected by the selection…

大面板中的简洁、无需特定参数设定的因果推断AI 解读

We propose a simple and transparent approach to causal inference using panel data with a large number of units and periods. Instead of imposing a particular parametric specification on the untreated potential outcomes, we assume they follow a nonparametric nonseparable factor model. We use long pre-treatment outcome histories to construct a measure of similarity between the latent factors of different units. Using this measure, we impute missing counterfactual means and…

高维模型中个体化分位数处理效应的极小极大速率最优推断AI 解读

The quantification of treatment effects plays an important role in a wide range of applications, including policy making and bio-pharmaceutical research. In this article, we study the quantile treatment effect (QTE) while addressing two specific types of heterogeneities: (a) personalized heterogeneity, which captures the varying treatment effects for different individuals, and (b) quantile heterogeneity, which accounts for how the impact of covariates varies across different…

局部干扰：半参数因果模型中干扰偏差的消除AI 解读

Interference bias is a major impediment to identifying causal effects in real-world settings. For example, vaccination reduces the transmission of a virus in a population such that everyone benefits -- even those who are not treated. This is a source of bias that must be accounted for if one wants to learn the true effect of a vaccine on an individual's immune system. Previous approaches addressing interference bias require strong domain knowledge in the form of a graphical…

临床试验中利用外部对照：目标量、估计与假设AI 解读

It is increasingly common to augment randomized controlled trial with external controls from observational data, to evaluate the treatment effect of an intervention. Traditional approaches to treatment effect estimation involve ambiguous estimands and unrealistic or strong assumptions, such as mean exchangeability. We introduce a double-indexed notation for potential outcomes to define causal estimands transparently and clarify distinct sources of implicit bias. We show that…

时变面板数据模型中潜在分组结构的估计AI 解读

We consider panel data models where coefficients change smoothly over time and follow a latent group structure, being homogeneous within but heterogeneous across groups. To jointly estimate the group membership and group-specific coefficient trajectories, we propose FUSE-TIME, a pairwise adaptive group fused-Lasso estimator combined with polynomial spline sieves. We establish consistency, derive the asymptotic distributions of the penalized sieve estimator and its…

用预后评分调整提升广义线性模型随机试验边际效应分析的功效AI 解读

In randomized clinical trials (RCTs), the accurate estimation of marginal treatment effects is crucial for determining the efficacy of interventions. Enhancing the statistical power of these analyses is a key objective for statisticians. The increasing availability of historical data from registries, prior trials, and health records presents an opportunity to improve trial efficiency. However, many methods for historical borrowing compromise strict type-I error rate control.…

非参数面板模型中平均处理效应的识别AI 解读

This paper studies identification of average treatment effects in a panel data setting. It introduces a novel nonparametric factor model and proves identification of average treatment effects. The identification proof is based on the introduction of a consistent estimator. Underlying the proof is a result that there is a consistent estimator for the expected outcome in the absence of the treatment for each unit and time period; this result can be applied more broadly, for…

断点回归设计中的噪声诱导随机化AI 解读

Regression discontinuity designs assess causal effects in settings where treatment is determined by whether an observed running variable crosses a pre-specified threshold. Here we propose a new approach to identification, estimation, and inference in regression discontinuity designs that uses knowledge about exogenous noise (e.g., measurement error) in the running variable. In our strategy, we weight treated and control units to balance a latent variable of which the running…

未观测混杂下多处理因果推断的copula敏感性分析AI 解读

Recent work has focused on the potential and pitfalls of causal identification in observational studies with multiple simultaneous treatments. Building on previous work, we show that even if the conditional distribution of unmeasured confounders given treatments were known exactly, the causal effects would not in general be identifiable, although they may be partially identified. Given these results, we propose a sensitivity analysis method for characterizing the effects of…

通过序贯调整混杂与样本流失恢复因果效应并开展推断AI 解读

Confounding bias and selection bias bring two significant challenges to the validity of conclusions drawn from applied causal inference. The latter can stem from informative missingness, such as in cases of attrition. We introduce the Sequential Adjustment Criteria (SAC), which extend available graphical conditions for recovering causal effects from confounding and attrition using sequential regressions, allowing for the inclusion of post-exposure and forbidden variables in…

联邦因果推断：超越元分析的多研究平均处理效应估计AI 解读

We study Federated Causal Inference, an approach to estimate treatment effects from decentralized data across centers. We compare three classes of Average Treatment Effect (ATE) estimators derived from the Plug-in G-Formula, ranging from simple meta-analysis to one-shot and multi-shot federated learning, the latter leveraging the full data to learn the outcome model (albeit requiring more communication). Focusing on Randomized Controlled Trials (RCTs), we derive the…

利用辅助变量提高时变处理效应估计效率AI 解读

Contextual sensing and delivery of digital interventions to improve health outcomes have gained significant traction in behavioral and psychiatric studies. Micro-randomized trials (MRTs) are a common experimental design for obtaining data-driven evidence on the effectiveness of digital interventions where each individual is repeatedly randomized to receive treatments over numerous time points. Throughout the study, individual characteristics and contextual factors around…

ICH E9（R1）框架下随机试验累积发生率与处理效应的推断AI 解读

In randomized controlled trials (RCTs) that focus on time-to-event outcomes, intercurrent events can arise in two ways: as semi-competing events, which modify the hazard of the primary outcome events, or as competing events, which make the definition of the primary outcome events unclear. Although five strategies have been proposed in the ICH E9 (R1) addendum to address intercurrent events in RCTs, these strategies are not easily applicable to time-to-event outcomes when…

给识别界加入协变量：究竟要回答什么问题？AI 解读

Symbolic nonparametric bounds for partial identification of causal effects now have a long history in the causal literature. Sharp bounds, bounds that use all available information to make the range of values as narrow as possible, are often the goal. For this reason, many publications have focused on deriving sharp bounds, but the concept of sharp bounds is nuanced and can be misleading. In settings with ancillary covariates, the situation becomes more complex. We provide…