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不同变量范式下深度学习因果发现的综述与路线图AI 解读

Understanding causality helps to structure interventions to achieve specific goals and enables predictions under interventions. With the growing importance of learning causal relationships, causal discovery tasks have transitioned from using traditional methods to infer potential causal structures from observational data to the field of pattern recognition involved in deep learning. The rapid accumulation of massive data promotes the emergence of causal search methods with…

将洛伦兹曲线的不对称性纳入经济不平等指标AI 解读

The Gini index signals only the dispersion of the distribution and is not very sensitive to income differences at the tails of the distribution. The widely used index of inequality can be adjusted to also measure distributional asymmetry by attaching weights to the distances between the Lorenz curve and the 45-degree line. The measure is equivalent to the Gini if the distribution is symmetric. The alternative measure of inequality inherits good properties from the Gini but…

结合基尼系数与角度衡量经济离散程度AI 解读

Classical measures of inequality use the mean as the benchmark of economic dispersion. They are not sensitive to inequality at the left tail of the distribution, where it would matter most. This paper presents a new inequality measurement tool that gives more weight to inequality at the lower end of the distribution, it is based on the comparison of all value pairs and synthesizes the dispersion of the whole distribution. The differences that sum to the Gini coefficient are…

改进的因果森林AI 解读

Uncovering the heterogeneity of causal effects of policies and business decisions at various levels of granularity provides substantial value to decision makers. This paper develops estimation and inference procedures for multiple treatment models in a selection-on-observed-variables framework by modifying the Causal Forest approach (Wager and Athey, 2018) in several dimensions. The new estimators have desirable theoretical, computational, and practical properties for…

f 敏感性模型下的敏感性分析：分布稳健视角AI 解读

This paper introduces the $f$-sensitivity model, a new sensitivity model that characterizes the violation of unconfoundedness in causal inference. It assumes the selection bias due to unmeasured confounding is bounded "on average"; compared with the widely used point-wise sensitivity models in the literature, it is able to capture the strength of unmeasured confounding by not only its magnitude but also the chance of encountering such a magnitude. We propose a framework for…

协变量自适应随机化下分位数处理效应的回归调整估计AI 解读

Datasets from field experiments with covariate-adaptive randomizations (CARs) usually contain extra covariates in addition to the strata indicators. We propose to incorporate these additional covariates via auxiliary regressions in the estimation and inference of unconditional quantile treatment effects (QTEs) under CARs. We establish the consistency and limit distribution of the regression-adjusted QTE estimator and prove that the use of multiplier bootstrap inference is…

利用正交性信息的适度平衡表征学习与处理效应估计AI 解读

Estimating the average treatment effect (ATE) from observational data is challenging due to selection bias. Existing works mainly tackle this challenge in two ways. Some researchers propose constructing a score function that satisfies the orthogonal condition, which guarantees that the established ATE estimator is "orthogonal" to be more robust. The others explore representation learning models to achieve a balanced representation between the treated and the controlled…

用于平均处理效应估计的稳健因果学习AI 解读

Many practical decision-making problems in economics and healthcare seek to estimate the average treatment effect (ATE) from observational data. The Double/Debiased Machine Learning (DML) is one of the prevalent methods to estimate ATE in the observational study. However, the DML estimators can suffer an error-compounding issue and even give an extreme estimate when the propensity scores are misspecified or very close to 0 or 1. Previous studies have overcome this issue…

连接因子模型与稀疏模型AI 解读

Factor and sparse models are two widely used methods to impose a low-dimensional structure in high-dimensions. However, they are seemingly mutually exclusive. We propose a lifting method that combines the merits of these two models in a supervised learning methodology that allows for efficiently exploring all the information in high-dimensional datasets. The method is based on a flexible model for high-dimensional panel data, called factor-augmented regression model with…

仅凭试验报告的二元亚组分析，部分识别个体化治疗反应AI 解读

Medical journals have adhered to a reporting practice that seriously limits the usefulness of published trial findings. Medical decision makers commonly observe many patient covariates and seek to use this information to personalize treatment choices. Yet standard summaries of trial findings only partition subjects into broad subgroups, typically into binary categories. Given this reporting practice, we study the problem of inference on long mean treatment outcomes…

加性暴露模型的无偏估计AI 解读

Causal inference methods have been applied in various fields where researchers want to estimate treatment effects. In traditional causal inference settings, one assumes that the outcome of a unit does not depend on treatments of other units. However, as causal inference methods are extended to more applications, there is a greater need for estimators of general causal effects. We use an exposure mapping framework [Aronow and Samii, 2017] to map the relationship between the…

因果推断框架综述AI 解读

Causal inference is a science with multi-disciplinary evolution and applications. On the one hand, it measures effects of treatments in observational data based on experimental designs and rigorous statistical inference to draw causal statements. One of the most influential framework in quantifying causal effects is the potential outcomes framework. On the other hand, causal graphical models utilizes directed edges to represent causalities and encodes conditional…

贪婪因果发现的几何结构AI 解读

Finding a directed acyclic graph (DAG) that best encodes the conditional independence statements observable from data is a central question within causality. Algorithms that greedily transform one candidate DAG into another given a fixed set of moves have been particularly successful, for example the GES, GIES, and MMHC algorithms. In 2010, Studený, Hemmecke and Lindner introduced the characteristic imset polytope, $\operatorname{CIM}_p$, whose vertices correspond to Markov…

极端连续处理效应：度量、估计与推断AI 解读

This paper concerns estimation and inference for treatment effects in deep tails of the counterfactual distribution of unobservable potential outcomes corresponding to a continuously valued treatment. We consider two measures for the deep tail characteristics: the extreme quantile function and the tail mean function defined as the conditional mean beyond a quantile level. Then we define the extreme quantile treatment effect (EQTE) and the extreme average treatment effect…

带噪声网络干扰下的因果推断AI 解读

Increasingly, there is a marked interest in estimating causal effects under network interference due to the fact that interference manifests naturally in networked experiments. However, network information generally is available only up to some level of error. We study the propagation of such errors to estimators of average causal effects under network interference. Specifically, assuming a four-level exposure model and Bernoulli random assignment of treatment, we…

电子健康记录中的树式亚组发现：含 DTG 疗法的治疗效应异质性AI 解读

The rich longitudinal individual level data available from electronic health records (EHRs) can be used to examine treatment effect heterogeneity. However, estimating treatment effects using EHR data poses several challenges, including time-varying confounding, repeated and temporally non-aligned measurements of covariates, treatment assignments and outcomes, and loss-to-follow-up due to dropout. Here, we develop the Subgroup Discovery for Longitudinal Data (SDLD) algorithm,…

时间序列中随机干预的因果效应：高温预警能否减少死亡和住院？AI 解读

The methodological development of this paper is motivated by the need to address the following scientific question: does the issuance of heat alerts prevent adverse health effects? Our goal is to address this question within a causal inference framework in the context of time series data. A key challenge is that causal inference methods require the overlap assumption to hold: each unit (i.e., a day) must have a positive probability of receiving the treatment (i.e., issuing a…

多重中介因果推断的贝叶斯非参数方法AI 解读

Mediation analysis with contemporaneously observed multiple mediators is an important area of causal inference. Recent approaches for multiple mediators are often based on parametric models and thus may suffer from model misspecification. Also, much of the existing literature either only allow estimation of the joint mediation effect, or, estimate the joint mediation effect as the sum of individual mediator effects, which often is not a reasonable assumption. In this paper,…

重复测量进展模型：估计进行性疾病的新型治疗效应AI 解读

Mixed Models for Repeated Measures (MMRMs) are ubiquitous when analyzing outcomes of clinical trials. However, the linearity of the fixed-effect structure in these models largely restrict their use to estimating treatment effects that are defined as linear combinations of effects on the outcome scale. In some situations, alternative quantifications of treatment effects may be more appropriate. In progressive diseases, for example, one may want to estimate if a drug has…

估计干扰参数如何降低方差并实现一致方差估计AI 解读

We often estimate a parameter of interest psi when the identifying conditions involve a nuisance parameter theta. Examples from causal inference are Inverse Probability Weighting, Marginal Structural Models and Structural Nested Models. To estimate treatment effects from observational data, these methods posit a (pooled) logistic regression model for the treatment and/or censoring probabilities and estimate these first. These methods are all based on unbiased estimating…

随机对照试验协变量平衡方法的对抗鲁棒性局限AI 解读

The first step towards investigating the effectiveness of a treatment via a randomized trial is to split the population into control and treatment groups then compare the average response of the treatment group receiving the treatment to the control group receiving the placebo. In order to ensure that the difference between the two groups is caused only by the treatment, it is crucial that the control and the treatment groups have similar statistics. Indeed, the validity and…

利用马尔可夫链描述企业规模转移动态AI 解读

Social employment, which is mostly carried by firms of different types, determines the prosperity and stability of a country. As time passing, the fluctuations of firm employment can reflect the process of creating or destroying jobs. Therefore, it is instructive to investigate the firm employment (size) dynamics. Drawing on the firm-level panel data extracted from the Chinese Industrial Enterprises Database 1998-2013, this paper proposes a Markov-chain-based descriptive…

高维数据下连续处理平均潜在结果的单调性检验AI 解读

While most treatment evaluations focus on binary interventions, a growing literature also considers continuously distributed treatments. We propose a Cramér-von Mises-type test for testing whether the mean potential outcome given a specific treatment has a weakly monotonic relationship with the treatment dose under a weak unconfoundedness assumption. In a nonseparable structural model, applying our method amounts to testing monotonicity of the average structural function in…

含辅助协变量的平滑概率 PARAFAC 模型AI 解读

In immunological and clinical studies, matrix-valued time-series data clustering is increasingly popular. Researchers are interested in finding low-dimensional embedding of subjects based on potentially high-dimensional longitudinal features and investigating relationships between static clinical covariates and the embedding. These studies are often challenging due to high dimensionality, as well as the sparse and irregular nature of sample collection along the time…