Identifying Conditional Causal Effects in MPDAGs
We consider identifying a conditional causal effect when a graph is known up to a maximally oriented partially directed acyclic graph (MPDAG). An MPDAG represents an equivalence class of graphs that is restricted by background knowledge and where all variables in the causal model are observed. We provide three results that address identification in this setting: an identification formula when the conditioning set is unaffected by treatment, a generalization of the well-known do calculus to the MPDAG setting, and an algorithm that is complete for identifying these conditional effects.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Do we become wiser with time? On causal equivalence with tiered background knowledge
Equivalence classes of DAGs (represented by CPDAGs) may be too large to provide useful causal information. Here, we address incorporating tiered background knowledge yielding restricted equivalence classes represented by…
Computational EfficiencyInformativenessvalidOn the Representation of Causal Background Knowledge and its Applications in Causal Inference
Causal background knowledge about the existence or the absence of causal edges and paths is frequently encountered in observational studies. The shared directed edges and links of a subclass of Markov equivalent DAGs ref…
Causal InferenceIdentifying Macro Conditional Independencies and Macro Total Effects in Summary Causal Graphs with Latent Confounding
Understanding causal relations in dynamic systems is essential in epidemiology. While causal inference methods have been extensively studied, they often rely on fully specified causal graphs, which may not always be avai…
Causal InferenceEpidemiologyIdentification and Estimation of Conditional Average Partial Causal Effects via Instrumental Variable
There has been considerable recent interest in estimating heterogeneous causal effects. In this paper, we study conditional average partial causal effects (CAPCE) to reveal the heterogeneity of causal effects with contin…
Identifying Macro Causal Effects in C-DMGs over DMGs
The do-calculus is a sound and complete tool for identifying causal effects in acyclic directed mixed graphs (ADMGs) induced by structural causal models (SCMs). However, in many real-world applications, especially in hig…