On Efficient Adjustment for Micro Causal Effects in Summary Causal Graphs
汇总因果图中微观因果效应的有效协变量调整AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要的核心是动态汇总因果图中的效应识别与协变量调整，图结构本身未表明存在处理干扰，因此归入因果推断。
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汇总因果图中微观因果效应的有效协变量调整
- 研究问题
- 当图节点代表整条时间序列且可能存在循环时，怎样更简单地判断跨期因果效应能否通过协变量调整识别，并选择较有效率的调整集？
- 方法与思路
- 在无隐藏混杂的设定下，作者重述汇总因果图中的既有可识别条件，提出覆盖更多有效调整集的新准则，并刻画准最优调整集。
- 材料陈述的贡献
- 摘要称新表述与原条件等价且更简单，扩大了可用调整集的范围，并在所刻画的集合中寻找最小化估计量渐近方差的调整集。
- 阅读关注点
- 应核对汇总图与底层动态 DAG 的对应、无隐藏混杂限制、新准则的完整性，以及准最优的比较范围和方差最小化条件。
- 阅读边界
- 所据为 arXiv 预印本，同行评审及期刊接收未经核实；摘要未给出具体图准则或计算复杂度，不能将准最优理解为无条件全局最优。
原文摘要
Observational studies in fields such as epidemiology often rely on covariate adjustment to estimate causal effects. Classical graphical criteria, like the back-door criterion and the generalized adjustment criterion, are powerful tools for identifying valid adjustment sets in directed acyclic graphs (DAGs). However, these criteria are not directly applicable to summary causal graphs (SCGs), which are abstractions of DAGs commonly used in dynamic systems. In SCGs, each node typically represents an entire time series and may involve cycles, making classical criteria inapplicable for identifying causal effects. Recent work established complete conditions for determining whether the micro causal effect of a treatment or an exposure $X_{t-γ}$ on an outcome $Y_t$ is identifiable via covariate adjustment in SCGs, under the assumption of no hidden confounding. However, these identifiability conditions have two main limitations. First, they are complex, relying on cumbersome definitions and requiring the enumeration of multiple paths in the SCG, which can be computationally expensive. Second, when these conditions are satisfied, they only provide two valid adjustment sets, limiting flexibility in practical applications. In this paper, we propose an equivalent but simpler formulation of those identifiability conditions and introduce a new criterion that identifies a broader class of valid adjustment sets in SCGs. Additionally, we characterize the quasi-optimal adjustment set among these, i.e., the one that minimizes the asymptotic variance of the causal effect estimator. Our contributions offer both theoretical advancement and practical tools for more flexible and efficient causal inference in abstracted causal graphs.