Parameterizing and Simulating from Causal Models
因果模型的参数化与模拟AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确围绕因果分布、平均因果效应及已处理者处理效应构造统计模型。
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因果模型的参数化与模拟
- 研究问题
- 当因果目标属于不同于观测分布的分布，且常是其边际量时，如何构造便于似然推断与模拟的参数化模型？
- 方法与思路
- 作者提出以因果目标为中心的简约参数化，再补全模型其余部分；离散变量使用优势比，连续变量使用 copula 等工具。
- 材料陈述的贡献
- 摘要称，该方法提供规则且无冗余的参数化构造，可模拟和拟合指定因果分布，并覆盖平均因果效应与已处理者处理效应等目标。
- 阅读关注点
- 应核对因果分布与观测分布的连接方式、参数空间的相容性，以及规则性、无冗余和似然计算成立的具体条件。
- 阅读边界
- 依据 arXiv 预印本摘要，同行评审及期刊接收未核实；各类因果目标所需识别条件及参数化限制，摘要未详细说明。
原文摘要
Many statistical problems in causal inference involve a probability distribution other than the one from which data are actually observed; as an additional complication, the object of interest is often a marginal quantity of this other probability distribution. This creates many practical complications for statistical inference, even where the problem is non-parametrically identified. In particular, it is difficult to perform likelihood-based inference, or even to simulate from the model in a general way. We introduce the `frugal parameterization', which places the causal effect of interest at its centre, and then builds the rest of the model around it. We do this in a way that provides a recipe for constructing a regular, non-redundant parameterization using causal quantities of interest. In the case of discrete variables we can use odds ratios to complete the parameterization, while in the continuous case copulas are the natural choice; other possibilities are also discussed. Our methods allow us to construct and simulate from models with parametrically specified causal distributions, and fit them using likelihood-based methods, including fully Bayesian approaches. Our proposal includes parameterizations for the average causal effect and effect of treatment on the treated, as well as other causal quantities of interest.