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用平均导数效应参数化连续处理的影响AI 解读

Summary The average treatment effect is commonly used to quantify the main effect of a binary treatment on an outcome. Extensions to continuous treatments are usually based on the dose-response curve or shift interventions, but both require strong overlap conditions, and the resulting curves may be difficult to summarize. This article focuses instead on average derivative effects, which are scalar estimands related to infinitesimal shift interventions requiring only local…

局部平均处理效应的随机化置信集合AI 解读

Summary We consider the problem of generating confidence sets in randomized experiments with noncompliance. We show that a refinement of a randomization-based procedure proposed by Imbens & Rosenbaum (2005) has desirable properties. Specifically, we show that using a studentized Anderson–Rubin statistic as a test statistic yields confidence sets that are finite-sample exact under treatment effect homogeneity and remain asymptotically valid for the local average treatment…

非线性遗憾下的处理选择AI 解读

Summary Following Savage (1951)and Manski (2004), the literature on statistical treatment choice focuses on the mean of welfare regret. Ignoring other features of the regret distribution, however, can lead to a rule that is sensitive to sampling uncertainty. We propose to minimize the mean of a nonlinear transformation of regret and show that singleton rules are not essentially complete for nonlinear regret. Focusing on mean-square regret, we derive closed-form fractions for…

利用代理控制结局的数据整合后推断AI 解读

Summary Data integration methods aim to extract low-dimensional embeddings from high-dimensional outcomes to remove unwanted variation, such as batch effects and unmeasured covariates, across heterogeneous datasets. However, multiple hypothesis testing after integration can be biased due to data-dependent processes. We introduce a robust post-integrated inference method that accounts for latent heterogeneity by leveraging control outcomes. Using causal interpretations, we…

校准敏感性模型AI 解读

Summary In causal inference, sensitivity models are used to assess how unmeasured confounders could alter causal analyses, but the sensitivity parameter (which quantifies the degree of unmeasured confounding) is often difficult to interpret. For this reason, researchers sometimes compare the sensitivity parameter to an estimate of measured confounding, a process known as calibration or benchmarking. However, calibrated estimates are not always interpreted correctly, and…

匹配估计量的自助法一致性AI 解读

Summary In a landmark paper, Abadie & Imbens (2008)showed that the naive bootstrap is inconsistent when applied to nearest neighbour matching estimators of the average treatment effect with a fixed number of matches. Since then, this finding has inspired numerous efforts to address the inconsistency issue, typically by employing alternative bootstrap methods. In contrast, in this work we show that the naive bootstrap is provably consistent for the original matching…

存在不依从时二水平多因素实验的因果效应界AI 解读

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

因果发现后因果效应的选择后推断AI 解读

Summary Algorithms for constraint-based causal discovery select graphical causal models among a space of possible candidates (e.g., all directed acyclic graphs) by executing a sequence of conditional independence tests. These may be used to inform the estimation of causal effects (e.g., average treatment effects) when there is uncertainty about which covariates ought to be adjusted for, or which variables act as confounders versus mediators. However, naively using the data…

回归图与诱导稀疏性的重参数化AI 解读

Summary That parameterization and sparsity are inherently linked raises the possibility that relevant models, not obviously sparse in their natural formulation, exhibit a population-level sparsity after reparameterization. In covariance models, positive definiteness enforces additional constraints on how sparsity can legitimately manifest. It is therefore natural to consider reparameterization maps in which sparsity respects positive definiteness. This paper provides insight…

协变量偏移下基于加权共形p值的无模型选择性推断AI 解读

Summary This paper introduces novel weighted conformal $ p $-values and methods for model-free selective inference. The problem is as follows: given test units with covariates $ X $ and missing responses $ Y $, how do we select units for which the responses $ Y $ are larger than user-specified values while controlling the proportion of false positives? Can we achieve this without modelling assumptions on the data and without restriction on the model for predicting the…

利用外部数据检验随机临床试验中的生物标志物交互疗效AI 解读

Summary In oncology the efficacy of novel therapeutics often differs across patient subgroups, and these variations are difficult to predict during the initial phases of the drug development process. The relation between the power of randomized clinical trials and heterogeneous treatment effects has been discussed by several authors. In particular, false negative results are likely to occur when the treatment effects concentrate in a subpopulation, but the study design did…

近端间接治疗比较AI 解读

Summary We consider the problem of indirect comparison, where a treatment arm of interest is absent by design in one randomized controlled trial, but available in the other. The former is the target trial, and the latter is the source trial. The identifiability of the target population average treatment effect often relies on conditional transportability assumptions. However, it is a common concern whether all relevant effect modifiers are measured and controlled for. We…

局部因果发现中的结构限制：识别目标变量的直接原因AI 解读

Summary We consider the problem of learning a set of direct causes of a target variable from an observational joint distribution. Learning directed acyclic graphs that represent the causal structure is a fundamental problem in science. Several results are known when the full directed acyclic graph is identifiable from the distribution, such as when a nonlinear Gaussian data-generating process is assumed. Here, we are interested only in identifying the direct causes of one…

连续处理后变量的主层分析：非参数识别与半参数估计AI 解读

Abstract Post-treatment variables often complicate causal inference. They appear in many scientific problems, including non-compliance, truncation by death, mediation, and surrogate endpoint evaluation. Principal stratification is a strategy to address these challenges by adjusting for the potential values of the post-treatment variables, defined as the principal strata. It allows for characterizing treatment effect heterogeneity across principal strata and unveiling the…

利用外部对照数据协调随机试验的亚组处理效应估计AI 解读

Abstract Subgroup analyses of randomized controlled trials (RCTs) constitute an important component of the drug development process in precision medicine. In particular, subgroup analyses of early-stage trials often influence the design and eligibility criteria of subsequent confirmatory trials and ultimately influence which subpopulations will receive the treatment after regulatory approval. However, subgroup analyses are often complicated by small sample sizes, which leads…

主分层因果中介分析的识别与多重稳健估计AI 解读

Abstract We consider assessing causal mediation in the presence of a posttreatment event (examples include noncompliance, a clinical event, or death). We identify natural mediation effects for the entire study population and for each principal stratum characterized by the joint potential values of the posttreatment event. We derive the efficient influence function for each mediation estimand, which motivates a set of multiply robust estimators for inference. The multiply…

Abstract We develop a general theory of omitted variable bias for a wide range of common causal parameters, including average treatment effects, average causal derivatives, and policy effects from covariate shifts. We show how plausibility judgments on the maximum explanatory power of omitted variables are sufficient to bound the bias, facilitating sensitivity analysis in otherwise complex models. Finally, we provide statistical inference methods that can leverage modern…

通过迭代扩展因果图选择混杂变量AI 解读

Confounder selection, namely choosing a set of covariates to control for confounding between a treatment and an outcome, is arguably the most important step in the design of an observational study. Previous methods, such as Pearl's back-door criterion, typically require pre-specifying a causal graph, which can often be difficult in practice. We propose an interactive procedure for confounder selection that does not require pre-specifying the graph or the set of observed…

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处理水平可能增长且组规模不等时设计型因果推断的 Berry–Esseen 界AI 解读

Neyman (1923/1990) introduced the randomization model, which contains the notation of potential outcomes to define causal effects and a framework for large-sample inference based on the design of the experiment. However, the existing theory for this framework is far from complete, especially when the number of treatment levels diverges and the treatment group sizes vary. We provide a unified discussion of statistical inference under the randomization model with general…

无需平滑参数的多元根号样本量一致匹配与逆密度加权期望估计AI 解读

Expected values weighted by the inverse of a multivariate density or, equivalently, Lebesgue integrals of regression functions with multivariate regressors occur in various areas of applications, including estimating average treatment effects, nonparametric estimators in random coefficient regression models or deconvolution estimators in Berkson errors-in-variables models. The frequently used nearest-neighbor and matching estimators suffer from bias problems in multiple…

利用双重机器学习估计平均偏效应AI 解读

Single-parameter summaries of variable effects in regression settings are desirable for ease of interpretation. However, (partially) linear models, for example, which would deliver these, may fit poorly to the data. On the other hand, an interpretable summary of the contribution of a given predictor is provided by the so-called average partial effect—the average slope of the regression function with respect to the predictor of interest. Although one can construct a doubly…

未观测个体异质性极端分位数的推断AI 解读

We develop a methodology for conducting inference on extreme quantiles of unobserved individual heterogeneity (e.g., heterogeneous coefficients and treatment effects) in panel data and meta-analysis settings. Inference is challenging in such settings: only noisy estimates of heterogeneity are available, and central limit approximations perform poorly in the tails. We derive a necessary and sufficient condition under which noisy estimates are informative about extreme…

利用正则化后分布回归评估最低工资的分布效应AI 解读

Summary We evaluate the distributional effects of a minimum wage introduction based on a dataset with a moderate sample size, but a large number of potential covariates. In this context, the selection of relevant control variables at each distributional threshold is crucial to test hypotheses about the impact of the continuous treatment variable. To this end, we use a post-double-selection logistic distribution regression approach, which allows for uniformly valid inference…

固定效应静态面板模型的双重机器学习AI 解读

Summary Recent advances in causal inference have seen the development of methods that make use of the predictive power of machine learning algorithms. In this paper, we develop novel double machine learning procedures for panel data in which these algorithms are used to approximate high-dimensional and non-linear nuisance functions of the covariates. Our new procedures are extensions of the well-known correlated random effects, within-group, and first-difference estimators…