REVIEW 4 cited by
Identifiability of Causal Graphs using Functional Models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
This work addresses the following question: Under what assumptions on the data generating process can one infer the causal graph from the joint distribution? The approach taken by conditional independence-based causal discovery methods is based on two assumptions: the Markov condition and faithfulness. It has been shown that under these assumptions the causal graph can be identified up to Markov equivalence (some arrows remain undirected) using methods like the PC algorithm. In this work we propose an alternative by defining Identifiable Functional Model Classes (IFMOCs). As our main theorem we prove that if the data generating process belongs to an IFMOC, one can identify the complete causal graph. To the best of our knowledge this is the first identifiability result of this kind that is not limited to linear functional relationships. We discuss how the IFMOC assumption and the Markov and faithfulness assumptions relate to each other and explain why we believe that the IFMOC assumption can be tested more easily on given data. We further provide a practical algorithm that recovers the causal graph from finitely many data; experiments on simulated data support the theoretical findings.
Forward citations
Cited by 4 Pith papers
-
Factored Causal Representation Learning for Robust Reward Modeling in RLHF
A factored causal representation learning method improves robustness of reward models in RLHF by isolating causal factors from biases like length and sycophancy using adversarial gradient reversal.
-
Learning Causal Graphs at Scale: A Foundation Model Approach
ADAG pre-trains a linear transformer to map observed data from many related tasks directly to DAG adjacency matrices, enabling fast zero-shot causal discovery on new order-consistent or heterogeneous datasets.
-
Real-Time Projected Adaptive Control for Closed-Chain Co-Manipulative Continuum Robots
A GVS-based projected adaptive controller stabilizes closed-chain continuum co-manipulation under unknown robot and object dynamics, with claimed Lyapunov tracking convergence and hardware validation.
-
Identifying Causal Direction via Dense Functional Classes
LCUBE picks the causal direction with the shorter MDL description of a cubic spline regression, and the paper claims an asymptotic identifiability guarantee under low-noise assumptions.
Discussion (0). Continue with ORCID to comment.