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控制理论与强化学习中决策相关可观测性的相对可忽略性框架AI 解读

Sequential decision-making systems routinely operate with missing or incomplete data. Classical reinforcement learning theory, which is commonly used to solve sequential decision problems, assumes Markovian observability, which may not hold under partial observability. Causal inference paradigms formalise ignorability of missingness. We show these views can be unified and generalized in order to guarantee Q-learning convergence even when the Markov property fails. To do so,…

利用概率模型与治疗选择准则构建网络荟萃分析的治疗层级AI 解读

A key output of network meta-analysis (NMA) is the relative ranking of treatments; nevertheless, it has attracted substantial criticism. Existing ranking methods often lack clear interpretability and fail to adequately account for uncertainty, over-emphasizing small differences in treatment effects. We propose a novel framework to estimate treatment hierarchies in NMA using a probabilistic model, focusing on a clinically relevant treatment-choice criterion (TCC). Initially,…

以剂量为工具变量、利用暴露数据稳健估计因果剂量响应关系AI 解读

An accurate estimation of the dose-response relationship is important to determine the optimal dose. For this purpose, a dose finding trial in which subjects are randomized to a few fixed dose levels is the most commonly used design. Often, the estimation uses response data only, although drug exposure data are often obtained during the trial. The use of exposure data to improve this estimation is difficult, as exposure-response relationships are typically subject to…

剂量—暴露—响应建模对剂量响应估计与优化的收益AI 解读

In randomized dose-finding trials, although drug exposure data form a part of key information for dose selection, the evaluation of the dose-response (DR) relationship often mainly uses DR data. We examine the benefit of dose-exposure-response (DER) modeling by sequentially modeling the dose-exposure (DE) and exposure-response (ER) relationships in parameter estimation and prediction, compared with direct DR modeling without PK data. We consider ER modeling approaches with…

评估处理效应异质性AI 解读

Heterogeneous treatment effects are of major interest in economics. For example, a poverty reduction measure would be best evaluated by its effects on those who would be poor in the absence of the treatment, or by the share among the poor who would increase their earnings because of the treatment. While these quantities are not identified, we derive nonparametrically sharp bounds using only the marginal distributions of the control and treated outcomes. Applications to…

医学研究中的回归不连续设计AI 解读

This article provides an introduction to the Regression Discontinuity (RD) design, and its application to empirical research in the medical sciences. While the main focus of this article is on causal interpretation, key concepts of estimation and inference are also briefly mentioned. A running medical empirical example is provided.

二元结果分层实验中平均处理效应的精确与保守推断AI 解读

We extend methods for finite-sample inference about the average treatment effect (ATE) in randomized experiments with binary outcomes to accommodate stratification (blocking). We present three valid methods that differ in their computational and statistical efficiency. The first method constructs conservative, Bonferroni-adjusted confidence intervals separately for the mean response in the treatment and control groups in each stratum, then takes appropriate weighted…

存在不依从的2^K析因实验中因果效应的界限AI 解读

Factorial experiments are ubiquitous in the social and biomedical sciences, but when units fail to comply with each assigned factors, identification and estimation of the average treatment effects become impossible without strong assumptions. Leveraging an instrumental variables approach, previous studies have shown how to identify and estimate the causal effect of treatment uptake among respondents who comply with treatment. A major caveat is that these identification…

带随机效应的生存时间与纵向结果因果联合模型：高维估计与变量选择AI 解读

We consider a joint survival and mixed-effects model to explain the survival time from longitudinal data and high-dimensional covariates in a population. The longitudinal data is modeled using a non linear mixed-effects model to account for the inter-individual variability in the population. The corresponding regression function serves as a link function incorporated into the survival model. In that way, the longitudinal data is related to the survival time. We consider a…

利用辅助总体工具变量识别因果效应与多重稳健估计AI 解读

Estimating causal effects in a target population with unmeasured confounders is challenging, especially when instrumental variables (IVs) are unavailable. However, IVs from auxiliary populations with similar problems can help infer causal effects in the target population. While the homogeneous conditional average treatment effect assumption has been widely used for effect transportability, it has not been explored in IV-based data fusion. We include it as a basic approach,…

同时反馈与未观测混杂下的同伴效应估计AI 解读

Estimating peer causal effects within complex real-world networks such as social networks is challenging, primarily due to simultaneous feedback between peers and unobserved confounders. Existing methods either address unobserved confounders while ignoring the simultaneous feedback, or account for feedback but under restrictive linear assumptions, thus failing to obtain accurate peer effect estimation. In this paper, we propose DIG2RSI, a novel Deep learning framework which…

停止时e-BH程序的任意时点有效错误发现率控制AI 解读

The recent e-Benjamini-Hochberg (e-BH) procedure for multiple hypothesis testing is known to control the false discovery rate (FDR) under arbitrary dependence between the input e-values. This paper points out an important subtlety when applying the e-BH procedure with e-processes, which are sequential generalizations of e-values (where the data are observed sequentially). Since adaptively stopped e-processes are e-values, the e-BH procedure can be repeatedly applied at every…

平行趋势违背下双重差分政策评估的贝叶斯敏感性分析AI 解读

Violations of the parallel trends assumption pose significant challenges for causal inference in difference-in-differences (DiD) studies, especially in policy evaluations where pre-treatment dynamics and external shocks may bias estimates. In this work, we propose a Bayesian DiD framework to allow us to estimate the effect of policies when parallel trends is violated. To address potential deviations from the parallel trends assumption, we introduce a formal sensitivity…

断点回归设计中的结构外推：学校支出公投应用AI 解读

We propose a structural approach to extrapolate average partial effects away from the cutoff in regression discontinuity designs (RDDs). Our focus is on applications that exploit closely contested school district referenda to estimate the effects of changes in education spending on local economic outcomes. We embed these outcomes in a spatial equilibrium model of local jurisdictions in which fiscal policy is determined by majority rule voting. This integration provides a…

非线性孟德尔随机化研究中的因果估计与推断AI 解读

Mendelian randomization (MR) is widely used to uncover causal relationships in the presence of unmeasured confounders. However, most existing MR methods presuppose linear causality, risking bias when the true relationships are nonlinear, which is a common empirical scenario. In this paper, we compared two prevalent instrumental variable techniques (the two-stage prediction method and the control function method) under both linear and nonlinear settings, and addressed key…

利用观测数据估计因果效应：农业与应用经济学研究指南AI 解读

Most research questions in agricultural and applied economics are of a causal nature, i.e., how one or more variables (e.g., policies, prices, the weather) affect one or more other variables (e.g., income, crop yields, pollution). Only some of these research questions can be studied experimentally. Most empirical studies in agricultural and applied economics thus rely on observational data. However, estimating causal effects with observational data requires appropriate…

基于协变量平衡倾向得分的双重差分估计量AI 解读

This article develops a covariate balancing approach for the estimation of treatment effects on the treated (ATT) in a difference-in-differences (DID) research design when panel data are available. We show that the proposed covariate balancing propensity score (CBPS) DID estimator possesses several desirable properties: (i) local efficiency, (ii) double robustness in terms of consistency, (iii) double robustness in terms of inference, and (iv) faster convergence to the ATT…

用于因果推断的结构保持表征学习神经网络AI 解读

Recent developments in causal inference have greatly shifted the interest from estimating the average treatment effect to the individual treatment effect. In this article, we improve the predictive accuracy of representation learning and adversarial networks in estimating individual treatment effects by introducing a structure keeper which maintains the correlation between the baseline covariates and their corresponding representations in the high dimensional space. We train…

基于锚定的因果设计：估计信念的影响AI 解读

A central challenge in any study of the effects of beliefs on outcomes, such as decisions and behavior, is the risk of omitted variables bias. Omitted variables, frequently unmeasured or even unknown, can induce correlations between beliefs and decisions that are not genuinely causal, in which case the omitted variables are referred to as confounders. To address the challenge of causal inference, researchers frequently rely on information provision experiments to randomly…

一般误差分布下贝叶斯因果发现的 DAG 选择一致性AI 解读

We consider the problem of learning the underlying causal structure among a set of variables, which are assumed to follow a Bayesian network or, more specifically, a linear recursive structural equation model (SEM) with the associated errors being independent and allowed to be non-Gaussian. A Bayesian hierarchical model is proposed to identify the true data-generating directed acyclic graph (DAG) structure where the nodes and edges represent the variables and the direct…

多频带可变滞后格兰杰因果：跨频率时间序列推断框架AI 解读

Understanding causal relationships in time series is fundamental to many domains, including neuroscience, economics, and behavioral science. Granger causality is one of the well-known techniques for inferring causality in time series. Typically, Granger causality frameworks have a strong fix-lag assumption between cause and effect, which is often unrealistic in complex systems. While recent work on variable-lag Granger causality (VLGC) addresses this limitation by allowing a…

用于过程追踪与其他单案例研究的 p 值AI 解读

We introduce a method for calculating \(p\)-values to test causal hypotheses in qualitative research \emph{a la} process tracing. As in an experiment, our \(p\)-value tells us how often one would make the same or more compelling observations favoring one theory while entertaining a rival theory. We adapt Fisher's (1935) randomization-based urn model to the reality of qualitative researchers, who cannot randomize history, but can make observations about historical processes.…

构造 g-computation 估计量：选择偏差的两个案例AI 解读

G-computation is a useful estimation method that can be adapted to address various biases in epidemiology. However, these adaptations may not be obvious for some complex causal structures. This challenge is an example of the much wider issue of translating a causal diagram into a novel estimation strategy. To highlight these challenges, we consider two recent cases from the selection bias literature: treatment-induced selection and co-occurrence of biases that lack a joint…

在因果决策中纳入结构不确定性AI 解读

Practitioners making decisions based on causal effects typically ignore structural uncertainty. We analyze when this uncertainty is consequential enough to warrant methodological solutions (Bayesian model averaging over competing causal structures). Focusing on bivariate relationships ($X \rightarrow Y$ vs. $X \leftarrow Y$), we establish that model averaging is beneficial when: (1) structural uncertainty is moderate to high, (2) causal effects differ substantially between…