Causal Dantzig: Fast inference in linear structural equation models with hidden variables under additive interventions
因果 Dantzig：加性干预下含隐藏变量线性结构方程模型的快速推断AI 解读 ↓
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因果 Dantzig：加性干预下含隐藏变量线性结构方程模型的快速推断
- 研究问题
- 利用不同观测环境的数据，如何在允许隐藏混杂的线性结构方程模型中进行因果推断，同时降低大规模计算负担？
- 方法与思路
- 作者对生成环境差异的干预类型施加更严格限制，以内积不变性构建因果 Dantzig 方法，讨论识别及低维、高维推断。
- 材料陈述的贡献
- 摘要称，给出低维渐近置信区间和高维有限样本界；不可识别时，解集在特定干预下仍有预测保证，并以模拟考察表现。
- 阅读关注点
- 应核对加性干预的限制、内积不变性和识别条件，以及隐藏变量的允许范围、置信区间条件与不可识别时预测保证的对象。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明。摘要未给出干预条件、误差界或运行时间，不能认定任意环境变化都能识别因果参数。
原文摘要
Causal inference is known to be very challenging when only observational data are available. Randomized experiments are often costly and impractical and in instrumental variable regression the number of instruments has to exceed the number of causal predictors. It was recently shown in Peters, Bühlmann and Meinshausen (2016) (J. R. Stat. Soc. Ser. B. Stat. Methodol. 78 947–1012) that causal inference for the full model is possible when data from distinct observational environments are available, exploiting that the conditional distribution of a response variable is invariant under the correct causal model. Two shortcomings of such an approach are the high computational effort for large-scale data and the assumed absence of hidden confounders. Here, we show that these two shortcomings can be addressed if one is willing to make a more restrictive assumption on the type of interventions that generate different environments. Thereby, we look at a different notion of invariance, namely inner-product invariance. By avoiding a computationally cumbersome reverse-engineering approach such as in Peters, Bühlmann and Meinshausen (2016), it allows for large-scale causal inference in linear structural equation models. We discuss identifiability conditions for the causal parameter and derive asymptotic confidence intervals in the low-dimensional setting. In the case of nonidentifiability, we show that the solution set of causal Dantzig has predictive guarantees under certain interventions. We derive finite-sample bounds in the high-dimensional setting and investigate its performance on simulated datasets.