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条件三重差分法AI 解读

Triple difference-in-differences designs are widely used to estimate causal effects in empirical work. Surveying the literature, we find that most applications include controls. We show that this standard practice is generally biased for the target causal estimand when covariate distributions differ across groups. To address this, we propose identifying a causal estimand by fixing the covariate distribution to that of one group. We then develop a double-robust estimator and…

用于双重差分的森林：超越参数模型的因果推断AI 解读

This paper introduces the Difference-in-Differences Bayesian Causal Forest (DiD-BCF), a novel non-parametric model addressing key challenges in DiD estimation, such as staggered adoption and heterogeneous treatment effects. DiD-BCF provides a unified framework for estimating Average (ATE), Group-Average (GATE), and Conditional Average Treatment Effects (CATE). A core innovation, its Parallel Trends Assumption (PTA)-based reparameterization, enhances estimation accuracy and…

多时期分布型双重差分模型AI 解读

Researchers are often interested in evaluating the impact of a policy on the entire (or specific parts of the) distribution of the outcome of interest. In this paper, I provide a method to recover the whole distribution of the untreated potential outcome for the treated group in non-experimental settings with staggered treatment adoption by generalizing the existing quantile treatment effects on the treated (QTT) estimator proposed by Callaway and Li (2019). Besides the QTT,…

合理处理效应路径的估计与可视化AI 解读

We consider point estimation and inference for the treatment effect path of a policy. Examples include dynamic treatment effects in microeconomics, impulse response functions in macroeconomics, and event study paths in finance. We present two sets of plausible bounds to quantify and visualize the uncertainty associated with this object. Both plausible bounds are often substantially tighter than traditional confidence intervals, and can provide useful insights even when…

利用因果图评估双重差分研究的平行趋势AI 解读

Difference-in-differences (DID) is popular because it can allow for unmeasured confounding when the key assumption of parallel trends holds. However, there exists little guidance on how to decide a priori whether this assumption is reasonable. We attempt to develop such guidance by considering the relationship between a causal diagram and the parallel trends assumption. This is challenging because parallel trends is scale-dependent and causal diagrams are generally…

利用随时有效秩检验开展实时项目评估AI 解读

Counterfactual mean estimators such as difference-in-differences and synthetic control have grown into workhorse tools for program evaluation. Inference for these estimators is well-developed in settings where all post-treatment data is available at the time of analysis. However, in settings where data arrives sequentially, these tests do not permit real-time inference, as they require a pre-specified sample size T. We introduce real-time inference for program evaluation…

交错采用双重差分中的融合扩展双向固定效应AI 解读

To address the bias of the canonical two-way fixed effects estimator for difference-in-differences under staggered adoptions, Wooldridge (2021) proposed the extended two-way fixed effects estimator, which adds many parameters. However, this reduces efficiency. Restricting some of these parameters to be equal (for example, subsequent treatment effects within a cohort) helps, but ad hoc restrictions may reintroduce bias. We propose a machine learning estimator with a single…

动态断点回归：事件研究方法AI 解读

I propose a novel argument to identify economically interpretable intertemporal treatment effects in dynamic regression discontinuity designs (RDDs). Specifically, I develop a dynamic potential outcomes model and reformulate two assumptions from the difference-in-differences literature, no anticipation and common trends, to attain point identification of cutoff-specific impulse responses. The estimand of each target parameter can be expressed as the sum of two static RDD…

用双重差分评估政策效应异质性驱动因素的因果框架AI 解读

Policymakers and researchers often seek to understand how a policy differentially affects a population and the pathways driving this heterogeneity. For example, when studying an excise tax on sweetened beverages, researchers might assess the roles of cross-border shopping, economic competition, and store-level price changes on beverage sales trends. However, traditional policy evaluation tools, like the difference-in-differences (DiD) approach, primarily target average…

匹配估计 ≤ 混合估计 ≤ 双重差分估计AI 解读

Since LaLonde's (1986) seminal paper, there has been ongoing interest in estimating treatment effects using pre- and post-intervention data. Scholars have traditionally used experimental benchmarks to evaluate the accuracy of alternative econometric methods, including Matching, Difference-in-Differences (DID), and their hybrid forms (e.g., Heckman et al., 1998b; Dehejia and Wahba, 2002; Smith and Todd, 2005). We revisit these methodologies in the evaluation of job training…

近似随机化检验中的聚类合并方法AI 解读

This paper develops procedures to combine clusters for the approximate randomization test proposed by Canay, Romano, and Shaikh (2017). Their test can be used to conduct inference with a small number of clusters and imposes weak requirements on the correlation structure. However, their test requires the target parameter to be identified within each cluster. A leading example where this requirement fails to hold is when a variable has no variation within clusters. For…

链式双重差分AI 解读

This paper studies the identification, estimation, and inference of long-term (binary) treatment effect parameters when balanced panel data is not available, or consists of only a subset of the available data. We develop a new estimator: the chained difference-in-differences, which leverages the overlapping structure of many unbalanced panel data sets. This approach consists in aggregating a collection of short-term treatment effects estimated on multiple incomplete panels.…

存在干扰时的双重差分AI 解读

In many scenarios, such as the evaluation of place-based policies, potential outcomes are not only dependent upon the unit's own treatment but also its neighbors' treatment. Despite this, "difference-in-differences" (DID) type estimators typically ignore such interference among neighbors. I show in this paper that the canonical DID estimators generally fail to identify interesting causal effects in the presence of neighborhood interference. To incorporate interference…

三重差分工具变量设计AI 解读

In this paper, we formalize a triple instrumented difference-in-differences (DID-IV). In this design, a triple Wald-DID estimand, which divides the difference-in-difference-in-differences (DDD) estimand of the outcome by the DDD estimand of the treatment, captures the local average treatment effect on the treated. The identifying assumptions mainly comprise a monotonicity assumption, and the common acceleration assumptions in the treatment and the outcome. We extend the…

存在多个事件的双重差分AI 解读

This paper studies staggered Difference-in-Differences (DiD) design when there is a second event confounding the target event. When the events are correlated, the treatment and the control group are unevenly exposed to the effects of the second event, causing an omitted event bias. To address this bias, I propose a two-stage DiD design. In the first stage, I estimate the combined effects of both treatments using a control group that is neither treated nor confounded. In the…

结局非随机缺失下的双重差分设计AI 解读

This paper addresses one of the most prevalent problems encountered by political scientists working with difference-in-differences (DID) design: missingness in panel data. A common practice for handling missing data, known as complete case analysis, is to drop cases with any missing values over time. A more principled approach involves using nonparametric bounds on causal effects or applying inverse probability weighting based on baseline covariates. Yet, these methods are…

事件研究剖析：假想实验、精确分解与权重诊断AI 解读

In recent decades, event studies have emerged as a central methodology in health and social research for evaluating the causal effects of staggered interventions. In this paper, we analyze event studies from experimental design principles for observational studies, with a focus on information borrowing across measurements. We develop robust weighting estimators that increasingly use more information across units and time periods, justified by increasingly stronger…

事件研究型合成双重差分估计量简论AI 解读

I propose an event study extension of Synthetic Difference-in-Differences (SDID) estimators. I show that, in simple and staggered adoption designs, estimators from Arkhangelsky et al. (2021) can be disaggregated into dynamic treatment effect estimators, comparing the lagged outcome differentials of treated and synthetic controls to their pre-treatment average. Estimators presented in this note can be computed using the sdid_event Stata package.

利用可能无效的工具变量实现识别AI 解读

This paper proposes a novel identification strategy relying on quasi-instrumental variables (quasi-IVs). A quasi-IV is a relevant but possibly invalid IV because it is not exogenous or not excluded. We show that a variety of models with discrete or continuous endogenous treatment which are usually identified with an IV - quantile models with rank invariance, additive models with homogenous treatment effects, and local average treatment effect models - can be identified under…

适用于随机阶梯楔形与观察性交错采用的广义双重差分估计AI 解读

Staggered treatment adoption arises in the evaluation of policy impact and implementation in many settings, including both randomized stepped-wedge trials and non-randomized quasi-experiments with panel data. In both settings, getting an interpretable, unbiased effect estimate requires careful consideration of the target estimand and possible treatment effect heterogeneities. This paper proposes a novel non-parametric approach to this estimation for either setting. By…

事件研究中的有限类型异质性处理效应AI 解读

A key assumption of the differences-in-differences designs is that the average evolution of untreated potential outcomes is the same across different treatment cohorts: a parallel trends assumption. In this paper, we relax the parallel trend assumption by assuming a latent type variable and developing a type-specific parallel trend assumption. With a finite support assumption on the latent type variable and long pretreatment time periods, we show that an extremum classifier…

rdid 与 rdidstag：稳健双重差分的 Stata 命令AI 解读

This article provides a Stata package for the implementation of the robust difference-in-differences (RDID) method developed in Ban and Kédagni (2023). It contains three main commands: $\texttt{rdid}$, $\texttt{rdid_dy}$, $\texttt{rdidstag}$, which we describe in the introduction and the main text. We illustrate these commands through simulations and empirical examples.

无需稀疏性假设的高维协变量局部投影推断AI 解读

This paper presents a comprehensive local projections (LP) framework for estimating future responses to current shocks, robust to high-dimensional controls without relying on sparsity assumptions. The approach is applicable to various settings, including impulse response analysis and difference-in-differences (DiD) estimation. While methods like LASSO exist, they often assume most parameters are exactly zero, limiting their effectiveness in dense data generation processes. I…

适用于重复截面数据的合成双重差分AI 解读

The synthetic difference-in-differences method provides an efficient method to estimate a causal effect with a latent factor model. However, it relies on the use of panel data. This paper presents an adaptation of the synthetic difference-in-differences method for repeated cross-sectional data. The treatment is considered to be at the group level so that it is possible to aggregate data by group to compute the two types of synthetic difference-in-differences weights on these…