Interpreting and using CPDAGs with background knowledge

2017/07/07 by Emilija Perković, Perković, Emilija, Markus Kalisch +3 · 2 citations
Computer Science · #AI-based Problem Solving and Planning #Bayesian Modeling and Causal Inference #FOS: Mathematics #Machine Learning and Algorithms #Statistics Theory (math.ST)

paper · pdf · doi:10.48550/arxiv.1707.02171

openalex publication_date 2017/07/07 · openalex created_date 2017/07/14 · openalex updated_date 2026/07/28

Abstract

We develop terminology and methods for working with maximally oriented partially directed acyclic graphs (maximal PDAGs). Maximal PDAGs arise from imposing restrictions on a Markov equivalence class of directed acyclic graphs, or equivalently on its graphical representation as a completed partially directed acyclic graph (CPDAG), for example when adding background knowledge about certain edge orientations. Although maximal PDAGs often arise in practice, causal methods have been mostly developed for CPDAGs. In this paper, we extend such methodology to maximal PDAGs. In particular, we develop methodology to read off possible ancestral relationships, we introduce a graphical criterion for covariate adjustment to estimate total causal effects, and we adapt the IDA and joint-IDA frameworks to estimate multi-sets of possible causal effects. We also present a simulation study that illustrates the gain in identifiability of total causal effects as the background knowledge increases. All methods are implemented in the R package pcalg.

Citations

• Causal Inference and Causal Explanation with Background Knowledge
• On the Validity of Covariate Adjustment for Estimating Causal Effects
• On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias
• A Complete Generalized Adjustment Criterion

Cited by

• Linear Causal Discovery with Interventional Constraints
• Tagged for Direction: Pinning Down Causal Edge Directions with Precision

Related

• Identifying causal effects in maximally oriented partially directed acyclic graphs
• Identifying Conditional Causal Effects in MPDAGs
• Practical Algorithms for Orientations of Partially Directed Graphical Models
• On the Representation of Pairwise Causal Background Knowledge and Its Applications in Causal Inference
• Minimal enumeration of all possible total effects in a Markov equivalence class