每日简报

利用处理前趋势进行双重差分推断AI 解读

Difference-in-differences (DID) are sometimes estimated with many pre-treatment periods. In such settings, the observed pre-treatment outcome evolutions provide direct information about the magnitude of shocks that could also occur after treatment. This paper proposes a simple inference procedure that uses those pre-trends as the reference distribution for the post-treatment DID. The procedure is closely related to existing conformal inference procedures, but its…

用于协变量平衡评估的加权 KS、CVM 与 AD 检验AI 解读

Assessing covariate balance is a core diagnostic step in causal inference, but commonly used summary measures can miss meaningful distributional differences they are not designed to detect. Distributional goodness-of-fit tests, including the Kolmogorov-Smirnov (KS), Anderson-Darling (AD), and Cramer-von Mises (CVM) tests, offer a more complete comparison but have previously been available only for unweighted data. We extend all three to accommodate case weights of any…

基于微观数据的一致半非参数需求估计AI 解读

This paper develops a profiled sieve minimum-distance estimator for a semi-nonparametric differentiated-products demand model with micro-level choice data. Building on Berry and Haile (2024), the estimator uses within-market variation in consumer covariates to recover a flexible consumer-heterogeneity function and market-specific composite intercepts. Excluded price instruments then separate these intercepts into a flexible price-side function and structural demand shocks.…

随机潜在选择与潜在结果AI 解读

Applied econometricians typically model each individual as having fixed outcomes under treatment and control and, in instrumental-variables (IV) settings, fixed treatment decisions under each value of the instrument. This paper asks what changes when outcomes and treatment allocations or choices are stochastic at the individual level. In the model, each individual has a stable (but possibly stochastic) response type consisting of two objects: a treatment choice probability…

存在无效工具变量时多重处理效应的识别与稳健推断AI 解读

The instrumental variable (IV) method is widely used to infer causal effects in observational studies with unmeasured confounding, but invalid instruments can compromise both population identification and finite-sample inference. This paper studies linear IV models with multiple endogenous treatments and possibly invalid instruments. Identification of multiple effects is more delicate than in the single-treatment setting because a single instrument no longer identifies a…

GAUGER：干扰下基于图加权暴露水平残差化的回归调整AI 解读

Estimating causal effects under interference is a common problem in social science and economics. However, it is challenging due to the complex dependency structure induced by network connections. In this paper, we propose GAUGER, a Generalized regression Adjustment framework via Graph-weighted Exposure-level Residualization for design-based causal inference under general interference. We first reveal a surprising mismatch between accuracy and efficiency in this setting:…

通过到高斯分布的 Wasserstein 距离进行无对比函数 ICA 与因果推断AI 解读

We study the squared $2$-Wasserstein distance to the standard Gaussian as a non-Gaussianity criterion and use it for linear Independent Component Analysis (ICA) and causal discovery in Linear Non-Gaussian Acyclic Models (LiNGAM). Unlike commonly used parametric contrasts and approximations of information-theoretic quantities, this criterion requires no distributional regularity beyond finite second moments, involves neither approximation nor tuning parameters, and can be…