Local Causal Discovery for Statistically Efficient Causal Inference

📰 ArXiv cs.AI

Local causal discovery enables statistically efficient causal inference by identifying valid adjustment sets without requiring the entire causal graph

advanced Published 1 Apr 2026

Action Steps

Identify target variables for causal effect estimation
Apply local causal discovery methods to find valid adjustment sets
Use the identified adjustment sets to estimate causal effects with low asymptotic variance
Evaluate the performance of local causal discovery methods compared to global methods

Who Needs to Know This

Data scientists and researchers on a team benefit from local causal discovery as it allows for efficient causal effect estimation, while machine learning engineers can apply these methods to improve model performance

Key Insight

💡 Local causal discovery can identify optimal adjustment sets without learning the entire causal graph, reducing computational complexity

Key Takeaways

Local causal discovery enables statistically efficient causal inference by identifying valid adjustment sets without requiring the entire causal graph

Full Article

Title: Local Causal Discovery for Statistically Efficient Causal Inference

Abstract:
arXiv:2510.14582v2 Announce Type: replace-cross Abstract: Causal discovery methods can identify valid adjustment sets for causal effect estimation for a pair of target variables, even when the underlying causal graph is unknown. Global causal discovery methods focus on learning the whole causal graph and therefore enable the recovery of optimal adjustment sets, i.e., sets with the lowest asymptotic variance, but they quickly become computationally prohibitive as the number of variables grows. Lo

Read full paper → ← Back to Reads