论文检索

基于观测分类数据的因果效应贝叶斯估计AI 解读

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…

缺失特定独立性与机制变化下的因果推断AI 解读

The recovery of causal effects in structural models with missing data often relies on $m$-graphs, which assume that missingness mechanisms do not directly influence substantive variables. Yet, in many real-world settings, missing data can alter decision-making processes, as the absence of key information may affect downstream actions and states. To overcome this limitation, we introduce $lm$-SCMs and $lm$-graphs, which extend $m$-graphs by integrating a label set that…

通过序贯调整混杂与样本流失恢复因果效应并开展推断AI 解读

Confounding bias and selection bias bring two significant challenges to the validity of conclusions drawn from applied causal inference. The latter can stem from informative missingness, such as in cases of attrition. We introduce the Sequential Adjustment Criteria (SAC), which extend available graphical conditions for recovering causal effects from confounding and attrition using sequential regressions, allowing for the inclusion of post-exposure and forbidden variables in…

分层获益概率：部分识别与稳健、稳定的推断AI 解读

We define the Probability of Tiered Benefit in scenarios with a binary exposure and an outcome that is either categorical with $K \geq 2$ ordered tiers or continuous partitioned by $K-1$ fixed thresholds into disjoint intervals. Similarly to other pure counterfactual queries, this parameter is not $g$-identifiable without additional assumptions. We demonstrate that strong monotonicity does not suffice for point identification when $K \geq 3$ and provide sharp bounds both…