Concave-monotone treatment response and monotone treatment selection: With an application to the returns to schooling
凹单调处理反应与单调处理选择:教育回报的应用AI 解读 ↓
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凹单调处理反应与单调处理选择:教育回报的应用AI 解读 ↓
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This paper identifies sharp bounds on the mean treatment response and average treatment effect under the assumptions of both the concave-monotone treatment response (concave-MTR) and the monotone treatment selection (MTS).We use our bounds and the U.S. National Longitudinal Survey of Youth 1979 to estimate mean returns to schooling.Our upper-bound estimates are substantially smaller than (i) estimates using only the concave-MTR assumption of Manski (1997), and (ii) estimates using only the MTR and MTS assumptions of Manski and Pepper (2000).Our upper-bound estimates fall in the range of the point estimates given in previous studies that assume linear wage functions.
伪面板事件研究:可信应用的计量经济学基础
Event studies with panel data are widely used to estimate treatment effects. With repeated cross-sections, future treatment dates are unobserved for untreated individuals. Pseudo-panel event studies impute pre-treatment outcomes of the treated using matched untreated individuals. This paper establishes identification in such designs and characterizes bias when identification conditions fail. I show that the pseudo-panel regression is equivalent to weighted least squares with…
This paper presents a causal decision framework for list-price elasticities in intermediated retail channels, where estimates are confounded by promotions, competitor moves and shopkeeper pass-through. Rather than forcing a number when the evidence is ambiguous, the system treats abstention (Wait) as an active diagnostic output. Built on Double Machine Learning and conformal prediction, it evaluates whether conditions exist to act on a recommended price or whether…
平均处理效应局部化:合成控制中的投影方法
Many real-world policies and business interventions require assessing short-term effects to inform timely decisions, even though most causal inference methods focus on long-term average treatment effects. In this article, we introduce average treatment effect localization (ATEL), which captures localized, short-term policy impacts in panel data settings with a single treated unit and provides early indicators of policy impact. To accommodate both time-varying and nonlinear…
自删失模型下信息性缺失的纵向因果推断
Longitudinal observational data are often subject to informative missingness. For example, in healthcare research using electronic health records, covariates and outcomes are measured only when patients interact with the healthcare system. Whether these data are observed usually depends on the unobserved variable values themselves, introducing a common form of data missing not at random. We develop identification and estimation methods for the effects of longitudinal…
存在干扰时合成控制的稳健回归方法
Synthetic control methods are widely used for policy evaluation, but most existing approaches rule out interference among units, compromising validity when such effects are present. We develop a framework that accommodates contaminated donor pools and unknown interference patterns through two stages: factor-model adjustment for unobserved confounding, followed by robust regression in which direct and interference effects appear as a sparse outlier component. We study two…
随时间刻画幸存者主层:识别、有效估计与敏感性分析
Longitudinal studies with functional outcomes subject to truncation-by-death frequently report time-indexed survivor average causal effect estimates, defined among individuals who would survive regardless of treatment assignment. Because the survivor stratum can change over time, the resulting effect estimates may pertain to populations with different characteristics. We propose accompanying survivor average causal effect estimates with measures of covariate divergence…