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重尾模型中的因果发现AI 解读

Causal questions are omnipresent in many scientific problems. While much progress has been made in the analysis of causal relationships between random variables, these methods are not well suited if the causal mechanisms only manifest themselves in extremes. This work aims to connect the two fields of causal inference and extreme value theory. We define the causal tail coefficient that captures asymmetries in the extremal dependence of two random variables. In the population…

因果自助法AI 解读

The bootstrap, introduced by The Jackknife, the Bootstrap and Other Resampling Plans ((1982), SIAM), has become a very popular method for estimating variances and constructing confidence intervals. A key insight is that one can approximate the properties of estimators by using the empirical distribution function of the sample as an approximation for the true distribution function. This approach views the uncertainty in the estimator as coming exclusively from sampling…

不规则识别与大量极端倾向得分下处理效应的有效推断AI 解读

This paper provides a framework for conducting valid inference for causal parameters without imposing strong variance or support restrictions on the propensity score. In particular, it covers the case of irregularly identified treatment effect parameters. We provide limit theorems for inverse probability weighting and doubly robust estimation of causal or counterfactual parameters that do not rely on trimming approaches. By construction the limiting distributions of these…

网络依赖随机变量的极限定理AI 解读

This paper is concerned with cross-sectional dependence arising because observations are interconnected through an observed network. Following (Doukhan and Louhichi, 1999), we measure the strength of dependence by covariances of nonlinearly transformed variables. We provide a law of large numbers and central limit theorem for network dependent variables. We also provide a method of calculating standard errors robust to general forms of network dependence. For that purpose,…

利用面板数据界定分布处理效应：失业冲击应用AI 解读

This paper develops new techniques to bound distributional treatment effect parameters that depend on the joint distribution of potential outcomes — an object not identified by standard identifying assumptions such as selection on observables or even when treatment is randomly assigned. I show that panel data and an additional assumption on the dependence between untreated potential outcomes for the treated group over time (i) provide more identifying power for…

用单一工具变量识别多个边际效应AI 解读

This paper proposes a new strategy for the identification of the marginal effects of an endogenous multivalued variable (which can be continuous, or a vector) in a model with an Instrumental Variable (IV) of lower dimension, which may even be a single binary variable, and multiple controls. Despite the failure of the classical order condition, we show that identification may be achieved by exploiting heterogeneity of the “first stage” in the controls through a new rank…

双边关系中由地理特征生成的工具变量何时无效？AI 解读

Summary In their highly influential paper, “Does Trade Cause Growth?,” Frankel and Romer estimate a trade equation to predict bilateral trade shares, which are in turn aggregated to construct an instrument for trade openness in income regressions. The Frankel‐Romer approach has gained widespread popularity as a method to generate instruments for trade and many other outcome variables from estimated bilateral relationships. In this paper, we show analytically and empirically…

异质性处理效应的准 Oracle 估计AI 解读

Summary Flexible estimation of heterogeneous treatment effects lies at the heart of many statistical applications, such as personalized medicine and optimal resource allocation. In this article we develop a general class of two-step algorithms for heterogeneous treatment effect estimation in observational studies. First, we estimate marginal effects and treatment propensities to form an objective function that isolates the causal component of the signal. Then, we optimize…

统一广义符号秩检验及其设计敏感度AI 解读

Summary A sensitivity analysis in an observational study tests whether the qualitative conclusions of an analysis would change if we were to allow for the possibility of limited bias due to confounding. The design sensitivity of a hypothesis test quantifies the asymptotic performance of the test in a sensitivity analysis against a particular alternative. We propose a new, nonasymptotic, distribution-free test, the uniform general signed rank test, for observational studies…

可能误设的高维模型中处理效应参数的推断AI 解读

Summary Eliminating the effect of confounding in observational studies typically involves fitting a model for an outcome adjusted for covariates. When, as often, these covariates are high-dimensional, this necessitates the use of sparse estimators, such as the lasso, or other regularization approaches. Naïve use of such estimators yields confidence intervals for the conditional treatment effect parameter that are not uniformly valid. Moreover, as the number of covariates…

内生社会网络中同伴效应的控制函数估计AI 解读

Abstract We propose methods of estimating the linear-in-means model of peer effects in which the peer group, defined by a social network, is endogenous in the outcome equation for peer effects. Endogeneity is due to unobservable individual characteristics that influence both link formation in the network and the outcome of interest. We propose two estimators of the peer effect equation that control for the endogeneity of the social connections using a control function…

控制变量、离散工具变量与结构函数识别AI 解读

Control variables provide an important means of controlling for endogeneity in econometric models with nonseparable and/or multidimensional heterogeneity. We allow for discrete instruments, giving identification results under a variety of restrictions on the way the endogenous variable and the control variables affect the outcome. We consider many structural objects of interest, such as average or quantile treatment effects. We illustrate our results with an empirical…

对 Kallus 及 Mo、Qi、Liu 的讨论：政策学习的新目标AI 解读

One of the key contributions that the causal inference literature has brought is a focus on clearly defined estimands. This focus has led to a real shift in the statistical approach (see, e.g., Dan...

利用随机化推断发现空气污染暴露效应的异质性AI 解读

Several studies have provided strong evidence that long-term exposure to air pollution, even at low levels, increases risk of mortality. As regulatory actions are becoming prohibitively expensive, robust evidence to guide the development of targeted interventions to protect the most vulnerable is needed. In this article, we introduce a novel statistical method that (i) discovers subgroups whose effects substantially differ from the population mean, and (ii) uses…

网络因果效应的自动 g 计算AI 解读

Methods for inferring average causal effects have traditionally relied on two key assumptions: (i) the intervention received by one unit cannot causally influence the outcome of another; and (ii) units can be organized into nonoverlapping groups such that outcomes of units in separate groups are independent. In this article, we develop new statistical methods for causal inference based on a single realization of a network of connected units for which neither assumption (i)…

学习最优分布鲁棒个体化治疗规则AI 解读

Recent development in the data-driven decision science has seen great advances in individualized decision making. Given data with individual covariates, treatment assignments and outcomes, policy makers best individualized treatment rule (ITR) that maximizes the expected outcome, known as the value function. Many existing methods assume that the training and testing distributions are the same. However, the estimated optimal ITR may have poor generalizability when the…

同伴效应观察性研究中的偏差与高维调整AI 解读

Peer effects, in which an individual’s behavior is affected by peers’ behavior, are posited by multiple theories in the social sciences. Randomized field experiments that identify peer effects, however, are often expensive or infeasible, so many studies of peer effects use observational data, which is expected to suffer from confounding. Here we show, in the context of information and media diffusion, that high-dimensional adjustment of a nonexperimental control group (660…

一类新的样本平均处理效应的统计推断AI 解读

We derive new variance formulas for inference on a general class of estimands of causal average treatment effects in a randomized control trial. We generalize the seminal work of Robins and show that when the researcher’s objective is inference on sample average treatment effect of the treated (SATT), a consistent variance estimator exists. Although this estimand is equal to the sample average treatment effect (SATE) in expectation, potentially large differences in both…

通过最优目标人群重设提高策略学习效率AI 解读

Nathan Kallus* School of Operations Research and Information Engineering and Cornell Tech, Cornell University, New York, NYColor versions of one or more of the figures in the article can be found online at www.tandfonline.com/r/JASA.Supplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.CONTACT Nathan Kallus kallus@cornell.edu School of Operations Research and Information Engineering and Cornell Tech, Cornell University, 2 West…

两阶段随机实验中存在干扰与不依从时的因果推断AI 解读

In many social science experiments, subjects often interact with each other and as a result one unit’s treatment influences the outcome of another unit. Over the last decade, a significant progress has been made toward causal inference in the presence of such interference between units. Researchers have shown that the two-stage randomization of treatment assignment enables the identification of average direct and spillover effects. However, much of the literature has assumed…

网络观察性研究中处理效应与干扰效应的识别和估计AI 解读

Laura Forastierea , Edoardo M. Airoldib* & Fabrizia Meallica Department of Biostatistics, Yale University, New Haven, CTb Department of Statistical Science, Fox School of Business, Temple University, Philadelphia, PAc Department of Statistics, Informatics, Applications “G. Parenti”, University of Florence, Florence, ItalySupplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.CONTACT Edoardo M. Airoldi airoldi@temple.edu…

兼顾平等的处理分配决策AI 解读

The goal of many randomized experiments and quasi-experimental studies in economics is to inform policies that aim to raise incomes and reduce economic inequality. A policy maximizing the sum of individual incomes may not be desirable if it magnifies economic inequality and post-treatment redistribution of income is infeasible. This article develops a method to estimate the optimal treatment assignment policy based on observable individual covariates when the policy…

重新考察食品价格对冲突的影响AI 解读

Studies that examine the impact of food prices on conflict usually assume that (all) changes in international food prices are exogenous shocks for individual countries or local areas. By isolating strictly exogenous shifts in global food commodity prices, we show that this assumption could seriously distort estimations of the impact on conflict in African regions. Specifically, we show that increases in food prices that are caused by harvest shocks outside Africa raise…

处理效应分析中联合分布泛函的尖锐界AI 解读

This article proposes an identification and estimation method that allows researchers to bound continuous functionals of the joint distribution of potential outcomes from the literature on treatment effects. The focus is on a model where no restrictions are imposed on treatment selection. The method can sharply bound interesting parameters when analytical bounds are difficult to derive, can be used in settings in which instruments are available, and can easily accommodate…