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基于核时序差分方法的最优策略评估AI 解读

We study nonparametric methods for estimating the value function of an infinite-horizon discounted Markov reward process (MRP). We analyze the kernel-based least-squares temporal difference (LSTD) estimate, which can be understood either as a nonparametric instrumental variables method, or as a projected approximation to the Bellman fixed point equation. Our analysis imposes no assumptions on the transition operator of the Markov chain, but rather only conditions on the…

配对整群随机试验中的统计推断AI 解读

This paper studies inference in cluster randomized trials where treatment status is determined according to a “matched pairs” design. Here, by a cluster randomized experiment, we mean one in which treatment is assigned at the level of the cluster; by a “matched pairs” design, we mean that a sample of clusters is paired according to baseline, cluster-level covariates and, within each pair, one cluster is selected at random for treatment. We study the large-sample behavior of…

为何研究复现率如此低？AI 解读

Many explanations have been offered for why replication rates are low in the social sciences, including selective publication, p -hacking, and treatment effect heterogeneity. This article emphasizes that issues with the most commonly used approach for setting sample sizes in replication studies may also play an important role. Theoretically, I show in a simple model of the publication process that we should expect the replication rate to fall below its nominal target, even…

从历史安慰剂对照试验推广活性对照的意向治疗效应：HPTN 084 案例AI 解读

In many clinical settings, an active-controlled trial design (e.g., a non-inferiority or superiority design) is often used to compare an experimental medicine to an active control (e.g., an FDA-approved, standard therapy). One prominent example is a recent phase 3 efficacy trial, HIV Prevention Trials Network Study 084 (HPTN 084), comparing long-acting cabotegravir, a new HIV pre-exposure prophylaxis (PrEP) agent, to the FDA-approved daily oral tenofovir disoproxil fumarate…

潜在异质环境中的强化学习AI 解读

Reinforcement Learning holds great promise for data-driven decision-making in various social contexts, including healthcare, education, and business. However, classical methods that focus on the mean of the total return may yield misleading results when dealing with heterogeneous populations typically found in large-scale datasets. To address this issue, we introduce the K-Value Heterogeneous Markov Decision Process, a framework designed to handle sequential decision…

在线学习中最优政策评价的双重稳健区间估计AI 解读

Evaluating the performance of an ongoing policy plays a vital role in many areas such as medicine and economics, to provide crucial instructions on the early-stop of the online experiment and timely feedback from the environment. Policy evaluation in online learning thus attracts increasing attention by inferring the mean outcome of the optimal policy (i.e., the value) in real-time. Yet, such a problem is particularly challenging due to the dependent data generated in the…

高斯链图模型的可识别性与一致估计AI 解读

The chain graph model admits both undirected and directed edges in one graph, where symmetric conditional dependencies are encoded via undirected edges and asymmetric causal relations are encoded via directed edges. Though frequently encountered in practice, the chain graph model has been largely under investigated in literature, possibly due to the lack of identifiability conditions between undirected and directed edges. In this paper, we first establish a set of novel…

具有非对称反事实效用的政策学习AI 解读

Data-driven decision making plays an important role even in high stakes settings like medicine and public policy. Learning optimal policies from observed data requires a careful formulation of the utility function whose expected value is maximized across a population. Although researchers typically use utilities that depend on observed outcomes alone, in many settings the decision maker’s utility function is more properly characterized by the joint set of potential outcomes…

集群随机实验中模型稳健且有效的协变量调整AI 解读

Cluster-randomized experiments are increasingly used to evaluate interventions in routine practice conditions, and researchers often adopt model-based methods with covariate adjustment in the statistical analyses. However, the validity of model-based covariate adjustment is unclear when the working models are misspecified, leading to ambiguity of estimands and risk of bias. In this article, we first adapt two conventional model-based methods, generalized estimating equations…

利用 Gram–Schmidt Walk 设计平衡随机实验协变量AI 解读

The design of experiments involves a compromise between covariate balance and robustness.This article provides a formalization of this tradeoff and describes an experimental design that allows experimenters to navigate it.The design is specified by a robustness parameter that bounds the worst-case mean squared error of an estimator of the average treatment effect.Subject to the experimenter's desired level of robustness, the design aims to simultaneously balance all linear…

部分干扰下的最低资源阈值政策AI 解读

When developing policies for prevention of infectious diseases, policymakers often set specific, outcome-oriented targets to achieve. For example, when developing a vaccine allocation policy, policymakers may want to distribute them so that at least a certain fraction of individuals in a census block are disease-free and spillover effects due to interference within blocks are accounted for. The article proposes methods to estimate a block-level treatment policy that achieves…

存在不依从与未知干扰时的因果推断AI 解读

We consider a causal inference model in which individuals interact in a social network and they may not comply with the assigned treatments. In particular, we suppose that the form of network interference is unknown to researchers. To estimate meaningful causal parameters in this situation, we introduce a new concept of exposure mapping, which summarizes potentially complicated spillover effects into a fixed dimensional statistic of instrumental variables. We investigate…

双边关系数据核密度估计的一致推断AI 解读

Dyadic data is often encountered when quantities of interest are associated with the edges of a network. As such it plays an important role in statistics, econometrics and many other data science disciplines. We consider the problem of uniformly estimating a dyadic Lebesgue density function, focusing on nonparametric kernel-based estimators taking the form of dyadic empirical processes. Our main contributions include the minimax-optimal uniform convergence rate of the dyadic…

强干扰实验的新估计目标AI 解读

In experiments that study social phenomena, such as peer influence or herd immunity, the treatment of one unit may influence the outcomes of others. Such “interference between units” violates traditional approaches for causal inference, so that additional assumptions are often imposed to model or limit the underlying social mechanism. For binary outcomes, we propose new estimands that can be estimated without such assumptions, allowing for interval estimates that assume only…

学习最优动态治疗方案时控制累积不良风险AI 解读

Dynamic treatment regimen (DTR) is one of the most important tools to tailor treatment in personalized medicine. For many diseases such as cancer and type 2 diabetes mellitus (T2D), more aggressive treatments can lead to a higher efficacy but may also increase risk. However, few methods for estimating DTRs can take into account both cumulative benefit and risk. In this work, we propose a general statistical learning framework to learn optimal DTRs that maximize the reward…

存在隐蔽混杂的因果网络发现与推断AI 解读

This article proposes a novel causal discovery and inference method called GrIVET for a Gaussian directed acyclic graph with unmeasured confounders. GrIVET consists of an order-based causal discovery method and a likelihood-based inferential procedure. For causal discovery, we generalize the existing peeling algorithm to estimate the ancestral relations and candidate instruments in the presence of hidden confounders. Based on this, we propose a new procedure for instrumental…

反事实插补：对可预测误差下反事实结果插补方法的评论AI 解读

The measurement of treatment (intervention) effects on a single (or just a few) treated unit(s) based on counterfactuals constructed from artificial controls has become a popular practice in applied statistics and economics since the proposal of the synthetic control method. However, most of the literature has ignored the time-series properties of the data. The work of Gonçalves and Ng fills this gap by proposing a simple correction for existing estimators to take into…

《误差可预测时的反事实结果插补》讨论：从处理选择理解 PUPAI 解读

I argue that the PUP approach proposed by Goncalves and Ng is not only useful for reducing the mean squared error of treatment effect estimators but can also be motivated from an identification perspective when units select into treatment based on past shocks.

《误差可预测时的反事实结果插补》讨论：误设与敏感性分析AI 解读

Gonçalves and Ng propose an interesting and simple way to improve counterfactual imputation methods when errors are predictable. For unconditional analyses, this approach yields smaller mean-squared error and tighter prediction intervals in large samples, even if the dependence of the errors is misspecified. For conditional analyses, this approach corrects the bias of standard methods, and provides valid asymptotic inference, if the dependence of the errors is correctly…

从双重差分与滞后因变量视角理解 PUPAI 解读

I discuss the practical unbiased predictor (PUP; Gonçalves and Ng) from the viewpoint of the literature on identification in event studies. The PUP can be seen as the prediction based on a generalized estimand that encompasses both the difference-in-differences (DID) and the lagged dependent variable (LDV). This feature of the PUP allows for a doubly robust property that the identification is achieved when either the parallel trend assumption or the LDV assumption holds at…

误差可预测时的反事实结果插补AI 解读

A crucial input into causal inference is the imputed counterfactual outcome. Imputation error can arise because of sampling uncertainty from estimating the prediction model using the untreated observations, or from out-of-sample information not captured by the model. While the literature has focused on sampling uncertainty, it vanishes with the sample size. Often overlooked is the possibility that the out-of-sample error can be informative about the missing counterfactual…

结果条件平均结构导数的识别与自动去偏机器学习AI 解读

This study proposes a new class of heterogeneous causal quantities, referred to as outcome-conditioned average structural derivatives (OASDs), in a general nonseparable model. An OASD is the average partial effect of a marginal change in a continuous treatment on individuals located on different parts of an outcome distribution, irrespective of individuals’ characteristics. We show that OASDs extend the unconditional quantile partial effects (UQPE) proposed by Firpo, Fortin,…

使用组别交互固定效应多层回归模型进行政策分析AI 解读

The use of multilevel regression models is prevalent in policy analysis to estimate the effect of group level policies on individual outcomes. In order to allow for the time varying effect of group heterogeneity and the group specific impact of time effects, we propose a group interactive fixed effects approach that employs interaction terms of group factor loadings and common factors in this model. For this approach, we consider the least squares estimator and associated…

双重差分估计中处理前趋势的等价性检验AI 解读

The plausibility of the “parallel trends assumption” in Difference-in-Differences estimation is usually assessed by a test of the null hypothesis that the difference between the average outcomes of both groups is constant over time before the treatment. However, failure to reject the null hypothesis does not imply the absence of differences in time trends between both groups. We provide equivalence tests that allow researchers to find evidence in favor of the parallel trends…