每日简报

条件影响函数AI 解读

There are many nonparametric objects of interest that are a function of a conditional distribution. One important example is an average treatment effect conditional on a subset of covariates. Many of these objects have a conditional influence function that generalizes the classical influence function of a functional of a (unconditional) distribution. Conditional influence functions have important uses analogous to those of the classical influence function. They can be used…

HNCI：高维网络因果推断AI 解读

The problem of evaluating the effectiveness of a treatment or policy commonly appears in causal inference applications under network interference. In this paper, we suggest the new method of high-dimensional network causal inference (HNCI) that provides both valid confidence interval on the average direct treatment effect on the treated (ADET) and valid confidence set for the neighborhood size for interference effect. We exploit the model setting in Belloni et al. (2022) and…

异质性处理效应估计器的可信评估AI 解读

Accurate heterogeneous treatment effect (HTE) estimation is essential for personalized recommendations, making it important to evaluate and compare HTE estimators. Traditional assessment methods are inapplicable due to missing counterfactuals. Current HTE evaluation methods rely on additional estimation or matching on test data, often ignoring the uncertainty introduced and potentially leading to incorrect conclusions. We propose incorporating uncertainty quantification into…

网络双重自回归AI 解读

Modeling high-dimensional time series with simple structures is a challenging problem. This paper proposes a network double autoregression (NDAR) model, which combines the advantages of network structure and the double autoregression (DAR) model, to handle high-dimensional, conditionally heteroscedastic, and network-structured data within a simple framework. The parameters of the model are estimated using quasi-maximum likelihood estimation, and the asymptotic properties of…

相依二元数据的因果发现AI 解读

The assumption of independence between observations (units) in a dataset is prevalent across various methodologies for learning causal graphical models. However, this assumption often finds itself in conflict with real-world data, posing challenges to accurate structure learning. We propose a decorrelation-based approach for causal graph learning on dependent binary data, where the local conditional distribution is defined by a latent utility model with dependent errors…

双臂最优臂识别中简单遗憾的极小极大最优性AI 解读

This study investigates an asymptotically minimax optimal algorithm in the two-armed fixed-budget best-arm identification (BAI) problem. Given two treatment arms, the objective is to identify the arm with the highest expected outcome through an adaptive experiment. We focus on the Neyman allocation, where treatment arms are allocated following the ratio of their outcome standard deviations. Our primary contribution is to prove the minimax optimality of the Neyman allocation…

PCM 选择器：用于线性因果效应评估的惩罚协变量与中介选择方法AI 解读

For a data-generating process for random variables that can be described with a linear structural equation model, we consider a situation in which (i) a set of covariates satisfying the back-door criterion cannot be observed or (ii) such a set can be observed, but standard statistical estimation methods cannot be applied to estimate causal effects because of multicollinearity/high-dimensional data problems. We propose a novel two-stage penalized regression approach, the…

遍历网络随机微分方程AI 解读

We propose a novel framework for Network Stochastic Differential Equations (N-SDE), where each node in a network is governed by an SDE influenced by interactions with its neighbors. The evolution of each node is driven by the interplay of three key components: the node's intrinsic dynamics (\emph{momentum effect}), feedback from neighboring nodes (\emph{network effect}), and a \emph{stochastic volatility} term modeled by Brownian motion. Our primary objective is to estimate…

随机效应面板模型中的参数特定偏差诊断AI 解读

The Hausman specification test assesses the random-effects specification by comparing the random-effects estimator with a fixed-effects alternative. This note shows how a recently proposed bias diagnostic for linear mixed models can complement that test in random-effects panel-data applications. The diagnostic delivers parameter-specific internal estimates of finite-sample bias, together with permutation-based $p$-values, from a single fitted random-effects model. We…

通过因果发现与领域知识整合提升神经网络预测表现AI 解读

In this paper, we develop a generic methodology to encode hierarchical causality structure among observed variables into a neural network in order to improve its predictive performance. The proposed methodology, called causality-informed neural network (CINN), leverages three coherent steps to systematically map the structural causal knowledge into the layer-to-layer design of neural network while strictly preserving the orientation of every causal relationship. In the first…

通过协变量选择优化平衡的自适应随机化方法AI 解读

Balancing influential covariates is crucial for valid treatment comparisons in clinical studies. While covariate-adaptive randomization is commonly used to achieve balance, its performance can be inadequate when the number of baseline covariates is large. It is therefore essential to identify the influential factors associated with the outcome and ensure balance among these critical covariates. In this article, we propose a novel adaptive randomization approach that…

多维不可观测异质性下的处理效应：边际处理效应识别AI 解读

This paper establishes sufficient conditions for the identification of the marginal treatment effects with multivalued treatments. Our model is based on a multinomial choice model with utility maximization. Our MTE generalizes the MTE defined in Heckman and Vytlacil (2005) in binary treatment models. As in the binary case, we can interpret the MTE as the treatment effect for persons who are indifferent between two treatments at a particular level. Our MTE enables one to…

识别问题的对抗式方法AI 解读

We introduce a new framework for characterizing identified sets of structural and counterfactual parameters in econometric models. By reformulating the identification problem as a set membership question, we leverage the separating hyperplane theorem in the space of observed probability measures to characterize the identified set through the zeros of a discrepancy function with an adversarial game interpretation. The set can be a singleton, resulting in point identification.…

生成式因果推断AI 解读

Generative Bayesian Computation (GBC) methods are developed for Casual Inference. Generative methods are simulation-based methods that use a large training dataset to represent posterior distributions as a map (a.k.a. optimal transport) to a base distribution. They avoid the use of MCMC by replacing the conditional posterior inference problem with a supervised learning problem. We further propose the use Quantile ReLU networks which are density free and hence apply in a…

回归不连续设计的半参数推断AI 解读

Treatment effects in regression discontinuity designs (RDDs) are often estimated using local regression methods. \cite{Hahn:01} demonstrated that the identification of the average treatment effect at the cutoff in RDDs relies on the unconfoundedness assumption and that, without this assumption, only the local average treatment effect at the cutoff can be identified. In this paper, we propose a semiparametric framework tailored for identifying the average treatment effect in…