The General-Liability Reform Experiments and the Distribution of Insurance-Market Outcomes
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一般责任法律改革与保险市场结果的分布AI 解读 ↓
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Many states enacted tort liability reform laws in the late 1980s to limit liability costs and stabilize insurance markets. This paper uses firm-level data from two states that enacted reforms over the 1984-91 period--New York and Colorado--to assess their effects. The liability insurance performance in Pennsylvania and Kentucky--two states that did not adopt reforms--provides a reference point for how insurance markets might have performed. The quantile regression models indicate that the improvement in insurer profitability was substantial. However, this improvement appears to be largely attributable to a secular trend rather than the effect of liability reforms.
组间不平衡下的风险异质性稳健推断
Population-level heterogeneity is ubiquitous in biomedical data, where differences across demographic or clinical subgroups can substantially alter risk patterns. For example, in intensive care unit (ICU) studies, the mortality risk associated with specific admission diagnoses can vary across ethnic groups. Existing approaches for detecting risk heterogeneity are often sensitive to baseline model misspecification and regularization bias, both of which commonly arise in…
The conditional average treatment effect (CATE) is frequently estimated in clinical studies to refute a homogeneous treatment effect hypothesis. Under this regime, all patients making up the population experience identical benefit from a given treatment relative to a comparator. Uncovering heterogeneous treatment effects through inference about the CATE, however, requires that covariates truly modifying the treatment effect be reliably collected at baseline. CATE-based…
非精确匹配何时能在无需调整的情况下保证平衡与有效推断?
One-to-one matching without replacement is a classical approach to constructing comparable treated and control samples in the design of observational studies. It pairs each treated unit with a distinct control while minimizing a covariate distance objective. With continuous covariates, the matched pairs generally remain inexact, which contributes to bias in downstream analysis. Its key theoretical properties, such as the resulting imbalance between matched pairs and when it…
多控制变量下工资不平等界的去偏推断
We study estimation and inference for a partially identified parameter whose identified set depends on a first-stage nuisance parameter that must itself be estimated. Combining the criterion-function approach with the theory of Neyman-orthogonal moments that underlies double/debiased machine learning, we propose a two-step procedure: the point-identified nuisance is estimated by flexible machine-learning methods, and the set-identified target is recovered as a level set of a…
农业政策干预的规模化效应
Policies to raise agricultural productivity have been central in the fight against global poverty. Their impacts are often measured in experiments that provide strong causal identification but may be too small-scale to capture general equilibrium effects that arise when policies are scaled up. We develop a quantitative model of agricultural trade, featuring a granular economic geography, that combines parameters estimated from small-scale experiments with rich administrative…
样本选择下观测数据中总体平均与分位处理效应的部分识别
ABSTRACT This article partially identifies population treatment effects in observational data under both non‐random treatment assignment and sample selection. Bounds are provided for both average and quantile population treatment effects, combining assumptions for the selected and the non‐selected subsamples. We show how different assumptions help narrow identification regions, and we illustrate our methods by partially identifying the effect of maternal education on the…