论文检索

双向固定效应面板数据的因果中介分析：双重/去偏机器学习方法AI 解读

We develop a double/debiased machine learning framework for causal mediation analysis in panel data with two-way fixed effects. Binary, multivalued, or continuous treatments and continuous mediators affect outcomes via histories. We formulate partially linear models for potential outcomes and mediators, define history-specific direct, indirect, and total effects, and identify them under a sequential conditional independence assumption given unit effects, allowing arbitrary…

基于图神经网络的网络数据因果中介分析AI 解读

Causal mediation analysis is typically formulated under no interference, an assumption often violated in networked populations. We develop a nonparametric framework for a single large observed network that allows simultaneous treatment and mediator spillovers and high-dimensional network confounding. Exposure and mediator mappings define causal estimands without restricting the true interference mechanism, separating own from spillover effects without prespecified…

内生性面板数据中连续处理效应的双重／去偏机器学习AI 解读

We propose a double/debiased machine learning framework to estimate average derivative effects in nonparametric panel models with two-way fixed effects. It extends instrumental variable methods to panel settings, handles continuous treatments and various forms of endogeneity, and introduces a cross-fitting scheme to restore independence after eliminating time fixed effects. A penalized GMM debiasing term enables automatic debiased machine learning with endogeneity. Our…

连续处理面板数据中的因果推断AI 解读

This paper proposes a framework that incorporates the two-way fixed effects model as a special case to conduct causal inference with a continuous treatment. Treatments are allowed to change over time and potential outcomes are dependent on historical treatments. Regression models on potential outcomes, along with the sequentially conditional independence assumptions (SCIAs) are introduced to identify the treatment effects, which are measured by aggre causal responses. Least…