Mediation analysis seeks to infer how much of the effect of an exposure on an
outcome can be attributed to specific pathways via intermediate variables or
mediators. This requires identification of so-called path-specific effects.
These express how a change in exposure affects those intermediate variables
(along certain pathways), and how the resulting changes in those variables in
turn affect the outcome (along subsequent pathways). However, unlike
identification of total effects, adjustment for confounding is insufficient for
identification of path-specific effects because their magnitude is also
determined by the extent to which individuals who experience large exposure
effects on the mediator, tend to experience relatively small or large mediator
effects on the outcome. This chapter therefore provides an accessible review of
identification strategies under general nonparametric structural equation
models (with possibly unmeasured variables), which rule out certain such
dependencies. In particular, it is shown which path-specific effects can be
identified under such models, and how this can be done.

本文研究中介效应分析的路径特定效应识别策略，并提出了一种通用的非参数结构方程模型。

中介分析的图形模型

Graphical models for mediation analysis

In this paper we discuss recent developments in econometrics that we view as
important for empirical researchers working on policy evaluation questions. We
focus on three main areas, where in each case we highlight recommendations for
applied work. First, we discuss new research on identification strategies in
program evaluation, with particular focus on synthetic control methods,
regression discontinuity, external validity, and the causal interpretation of
regression methods. Second, we discuss various forms of supplementary analyses
to make the identification strategies more credible. These include placebo
analyses as well as sensitivity and robustness analyses. Third, we discuss
recent advances in machine learning methods for causal effects. These advances
include methods to adjust for differences between treated and control units in
high-dimensional settings, and methods for identifying and estimating
heterogeneous treatment effects.

这篇论文讨论了计量经济学的三个主要领域，分别是政策评估、识别策略和机器学习。论文详细解释了在每个领域中应用的最新技术以及建议。