每日简报

垄断竞争下利用收入数据识别和估计生产函数与消费者需求AI 解读

We establish nonparametric identification of production functions, total factor productivity (TFP), price markups, and firms' output prices and quantities, as well as consumer demand, using firm-level revenue data, without observing output quantity, in a monopolistically competitive environment with a fully nonparametric demand system. This result overturns the widely held view -- formalized by Bond, Hashemi, Kaplan, and Zoch (2021) -- that output elasticities and markups…

拓扑因果效应AI 解读

Estimating causal effects is particularly challenging when outcomes arise in complex, non-Euclidean spaces, where conventional methods often fail to capture meaningful structural variation. We develop a framework for topological causal inference that defines treatment effects through differences in the topological structure of potential outcomes, summarized by power-weighted silhouette functions of persistence diagrams. We develop an efficient, doubly robust estimator in a…

从观察数据估计广告视觉属性效应：生成图像辅助的双重机器学习AI 解读

Digital advertising increasingly relies on visual content, yet marketers lack rigorous methods for understanding how specific visual attributes causally affect consumer engagement. This paper addresses a fundamental methodological challenge: estimating causal effects when the treatment, such as a model's skin tone, is an attribute embedded within the image itself. Standard approaches like Double Machine Learning (DML) fail in this setting because vision encoders entangle…

静态支付意愿何时产生误导？动态特征价值评估框架AI 解读

Many policy counterfactuals depend on how consumers value product attributes such as sugar, caffeine, alcohol, or emissions. Standard hedonic and differentiated-products models typically impose time-separable preferences. But when attributes are habit forming, current consumption can shift future marginal valuations, so static willingness-to-pay may be insufficient for policy counterfactuals. I develop a nonparametric revealed-preference framework for dynamic hedonic…

异质性处理效应变量重要性的目标学习AI 解读

Quantifying the heterogeneity of treatment effect is important for understanding how a commercial product or medical treatment affects different population subgroups. While much of treatment effect heterogeneity analysis focuses on the conditional average treatment effect, an alternative parameter that captures treatment effect heterogeneity is the variance of treatment effect across different covariate groups. One can also derive variable importance parameters that measure…

含处理与协变量交互项的两阶段最小二乘及处理效应异质性AI 解读

Treatment effect heterogeneity with respect to covariates is common in instrumental variable (IV) analyses. An intuitive approach, which we call the interacted two-stage least squares (2sls), is to postulate a working linear model of the outcome on the treatment, covariates, and treatment-covariate interactions, and instrument it using the IV, covariates, and IV-covariate interactions. We clarify the causal interpretation of the interacted 2sls under the local average…

利用结局一致配对信息改进条件 Logistic 回归及其软件实现AI 解读

We develop an improvement to conditional logistic regression (CLR) in the setting where the parameter of interest is the additive effect of binary treatment effect on log-odds of the positive level in the binary response. Our improvement is simply to use information learned above the nuisance control covariates found in the concordant response pairs' observations (which is usually discarded) to create an informative prior on their coefficients. This prior is then used in the…

通过设计型市场进行干扰下的因果推断AI 解读

In auction and matching markets, estimating the welfare effects of demand-side treatments is challenging because of spillovers through the mechanism. We develop a quasi-experimental approach that avoids parametric assumptions typically imposed by structural methods. For a class of strategy-proof "cutoff" mechanisms, we propose an estimator that runs a weighted and perturbed version of the mechanism on data from a single market. The estimator is semi-parametrically efficient,…