Bayesian estimation of causal effects from observational categorical data
基于观测分类数据的因果效应贝叶斯估计AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要直接研究观测分类数据中的后门调整、因果图不确定性与干预效应估计。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
基于观测分类数据的因果效应贝叶斯估计
- 研究问题
- 在不存在潜在混杂、但因果贝叶斯网络未知的高维分类变量系统中，如何估计变量对之间的干预效应并表达图结构不确定性？
- 方法与思路
- 作者结合贝叶斯后门估计与模型平均，构造干预分布后验；通过直接抽样和 MCMC 计算目标量，并以 Jensen–Shannon 散度定义效应。
- 材料陈述的贡献
- 摘要称该方法将贝叶斯 IDA 扩展到分类数据，给出部分矩的闭式表达及可扩展计算方案，模拟中点估计与强效应发现表现有利。
- 阅读关注点
- 应核对因果图与参数先验、模型平均范围、调整集选择和 MCMC 收敛，并检查散度型效应如何解释及模拟比较的具体设置。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实。无潜在混杂是明确前提；摘要未说明其他识别条件，模拟表现不代表普遍优越性。
原文摘要
We present a Bayesian procedure for estimation of pairwise intervention effects in a high-dimensional system of categorical variables. We assume that we have observational data generated from an unknown causal Bayesian network for which there are no latent confounders. Most of the existing methods developed for this setting assume that the underlying model is linear Gaussian, including the Bayesian IDA (BIDA) method that we build upon in this work. By combining a Bayesian backdoor estimator with model averaging, we obtain a posterior over the intervention distributions of a cause-effect pair that can be expressed as a mixture over stochastic linear combinations of Dirichlet distributions. Although there is no closed-form expression for the posterior density, it is straightforward to produce Monte Carlo approximations of target quantities through direct sampling, and we also derive closed-form expressions for a few selected moments. To scale up the proposed procedure, we employ Markov Chain Monte Carlo (MCMC), which also enables us to use more efficient adjustment sets compared to the current exact BIDA. Finally, we use Jensen-Shannon divergence to define a novel causal effect based on a set of intervention distributions in the general categorical setting. We compare our method to the original IDA method and existing Bayesian approaches in numerical simulations and show that categorical BIDA performs favorably against the existing alternative methods in terms of producing point estimates and discovering strong effects.