2023/02/16 by Shantanu Gupta, Gupta, Shantanu, David Childers +3 · 1 voice · 2 citations
Computer Science · Decision Sciences · Mathematics · #Advanced Causal Inference Techniques #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Methodology (stat.ME) #Scientific Computing and Data Management #cs.LG #stat.ME
paper · pdf · doi:10.48550/arxiv.2302.08070
openalex publication_date 2023/02/16 · arxiv published 2023/02/16 · arxiv updated 2024/04/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Even when the causal graph underlying our data is unknown, we can use observational data to narrow down the possible values that an average treatment effect (ATE) can take by (1) identifying the graph up to a Markov equivalence class; and (2) estimating that ATE for each graph in the class. While the PC algorithm can identify this class under strong faithfulness assumptions, it can be computationally prohibitive. Fortunately, only the local graph structure around the treatment is required to identify the set of possible ATE values, a fact exploited by local discovery algorithms to improve computational efficiency. In this paper, we introduce Local Discovery using Eager Collider Checks (LDECC), a new local causal discovery algorithm that leverages unshielded colliders to orient the treatment's parents differently from existing methods. We show that there exist graphs where LDECC exponentially outperforms existing local discovery algorithms and vice versa. Moreover, we show that LDECC and existing algorithms rely on different faithfulness assumptions, leveraging this insight to weaken the assumptions for identifying the set of possible ATE values.