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异质性部分干扰下观察性研究处理效应的半参数估计AI 解读

In many observational studies in social science and medicine, subjects or units are connected, and one unit’s treatment and attributes may affect another’s treatment and outcome, violating the stable unit treatment value assumption (SUTVA) and resulting in interference. To enable feasible estimation and inference, many previous works assume exchangeability of interfering units (neighbors). However, in many applications with distinctive units, interference is heterogeneous…

三重稳健面板估计量AI 解读

ABSTRACT This paper studies estimation of causal effects in a panel data setting. We introduce a new estimator, the Triply Robust Panel (TROP) estimator, that combines a flexible model for the potential outcomes based on a low‐rank factor structure on top of a two‐way‐fixed effect specification, with unit weights intended to upweight units similar to the treated units and time weights intended to upweight time periods close to the treated time periods. We study the…

多重随机化设计：存在干扰时的估计与推断AI 解读

Abstract Completely randomized experiments, originally developed by Fisher and Neyman in the 1930s, are still widely used in practice, even in online experimentation. However, such designs are of limited value for answering standard questions in marketplaces, where multiple populations of agents interact strategically, leading to complex patterns of spillover effects. In this article, we derive the finite-sample properties of tractable estimators for ‘Simple Multiple…

持续混杂下通过数据结合开展长期因果推断AI 解读

Abstract We study the identification and estimation of long-term treatment effects by combining short-term experimental data and long-term observational data subject to unobserved confounding. This problem arises often when concerned with long-term treatment effects since experiments are often short-term due to operational necessity while observational data can be more easily collected over longer time frames but may be subject to confounding. In this paper, we tackle the…

Summary In this survey we discuss the recent causal panel data literature. This recent literature has focused on credibly estimating causal effects of binary interventions in settings with longitudinal data, emphasising practical advice for empirical researchers. It pays particular attention to heterogeneity in the causal effects, often in situations where few units are treated and with particular structures on the assignment pattern. The literature has extended earlier work…

因果面板数据模型的矩阵补全方法AI 解读

In this paper we study methods for estimating causal effects in settings with panel data, where some units are exposed to a treatment during some periods and the goal is estimating counterfactual (untreated) outcomes for the treated unit/period combinations. We propose a class of matrix completion estimators that uses the observed elements of the matrix of control outcomes corresponding to untreated unit/periods to impute the "missing" elements of the control outcome matrix,…

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…

为何不应在断点回归设计中使用高阶多项式AI 解读

It is common in regression discontinuity analysis to control for third, fourth, or higher-degree polynomials of the forcing variable. There appears to be a perception that such methods are theoretically justified, even though they can lead to evidently nonsensical results. We argue that controlling for global high-order polynomials in regression discontinuity analysis is a flawed approach with three major problems: it leads to noisy estimates, sensitivity to the degree of…

The increasing popularity of regression discontinuity methods for causal inference in observational studies has led to a proliferation of different estimating strategies, most of which involve first fitting nonparametric regression models on both sides of a treatment assignment boundary and then reporting plug-in estimates for the effect of interest. In applications, however, it is often difficult to tune the nonparametric regressions in a way that is well calibrated for the…

多工具变量下的随机效应估计量AI 解读

In this paper we propose a new estimator for a model with one endogenous regressor and many instrumental variables. Our motivation comes from the recent literature on the poor properties of standard instrumental variables estimators when the instrumental variables are weakly correlated with the endogenous regressor. Our proposed estimator puts a random coefficients structure on the relation between the endogenous regressor and the instruments. The variance of the random…

补贴培训对受训者收入分位数影响的工具变量估计AI 解读

The effect of government programs on the distribution of participants' earnings is important for program evaluation and welfare comparisons.This paper reports es- timates of the effects of JTPA training programs on the distribution of earnings.The estimation uses a new instrumental variable (IV) method that measures program impacts on the quantiles of outcome variables.This quantile treatment effects (QTE) estimator accommodates exogenous covariates and reduces to quantile…